diff --git a/.ipynb_checkpoints/README-checkpoint.md b/.ipynb_checkpoints/README-checkpoint.md new file mode 100644 index 0000000..6bdcca8 --- /dev/null +++ b/.ipynb_checkpoints/README-checkpoint.md @@ -0,0 +1,65 @@ +# dGPredictor + +================================== +### Requirements: + +1. Python 3.8.10 +2. RDkit (http://www.rdkit.org/) +3. pandas (https://pandas.pydata.org/) +4. matplotlib (https://matplotlib.org/stable/users/installing.html) +5. Scikit-learn (https://scikit-learn.org/stable/) +6. Streamlit (https://streamlit.io/) +7. Openbabel (https://anaconda.org/openbabel/openbabel) +8. ChemAxon's Marvin >= 5.11 +9. Pulp + +Installation +1. Python 3.8.10 (https://www.python.org/downloads/windows/) +Recommended- +- Create anaconda environment using command "conda create -n dGPredictor python=3.8 ipython" +- activate the env using command "conda activate dGPredictor" or "source activate dGPredictor" +2. RDkit +- type command "conda install -c conda-forge rdkit" in your dGPredictor env to install rdkit +3. Pandas +- "conda install pandas" +4. matplotlib +- "conda install -c conda-forge matplotlib" +5. Scikit-learn +- use command "pip install -U scikit-learn" +6. Streamlit +- use command "pip install -U streamlit" +7. Openbabel +- run "conda install -c conda-forge openbabel" +- Installation via "pip" may require a wheel binary from this site https://www.lfd.uci.edu/~gohlke/pythonlibs/#openbabel, since the PyPI version may yield a "SWIG failed" installation error. +8. ChemAxon's Marvin (PkA value estimation) +- Marvin is only required for adding structures of novel metabolites/compounds that are not in the KEGG database +- instructions (https://chemaxon.com/products/marvin/download) +- add "cxcalc.bat (macOS) /cxcalc.exe (Windows)" to PATH and also in "./CC/chemaxon.py" file +- you will need to get a license to use ChemAxon (it is free for academic use) +9. Pulp +- use command "pip install -U pulp" + + + + +================================== +### Running web-interface loacally using streamlit + +- Model generation: Run "model_gen.py" using "python model_gen.py" once to create dGPredictor model file :- (Running this might take some time) +- run "streamlit run ./streamlit/main.py" from dGPredictor folder +- running KEGG reaction (doesn't require ChemAxon's Marvin) : copy paste the reaction equation into reaction section and click search + +### Gibbs free energy prediction use automated group decomposition method + +- Step 1: decompose the metabolites based on smiles files (see function decompse_ac in decompose_groups.py or notebook ) +- Step 2: create group changes vectors (i.e. reaction rules) based on group changes in metabolites of reactions (see get_rxn_rule in decompose_groups.py) +- Step 3: linear regression, Ridge Regression and Bayesian Ridge Regression in "predict.py" +- Step 4: Multiple regression models in notebook "analysis_dGPredictor.ipynb" + +### Pathway design using novoStoic +- Run "mini_novoStoic.py" to see an example to design pathways for Isobutanol synthesis + + +# demo +![dGPredictor Demo](figures/dg_demo_py3.gif) + diff --git a/.ipynb_checkpoints/analysis_dGPredictor-checkpoint.ipynb b/.ipynb_checkpoints/analysis_dGPredictor-checkpoint.ipynb new file mode 100644 index 0000000..71d22f5 --- /dev/null +++ b/.ipynb_checkpoints/analysis_dGPredictor-checkpoint.ipynb @@ -0,0 +1,1003 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.io import savemat, loadmat\n", + "import pandas as pd\n", + "import pdb\n", + "import json\n", + "import numpy as np\n", + "from numpy import median, mean\n", + "from sklearn.linear_model import BayesianRidge, LinearRegression, RidgeCV, Ridge\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", + "from sklearn.model_selection import cross_val_score, LeaveOneOut\n", + "import pickle\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## group contribution method linear regression " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean squared error: 45.20\n", + "Coefficient of determination: 0.9989\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.7, 0.25, '$R^2$ = 0.9989')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ac = loadmat('./data/component_contribution_python.mat')\n", + "\n", + "S = ac['train_S']\n", + "G = ac['G']\n", + "b = ac['b']\n", + "\n", + "m, n = S.shape\n", + "assert G.shape[0] == m\n", + "assert b.shape == (n, 1)\n", + "\n", + "STG = np.dot(S.T,G)\n", + "\n", + "X = STG\n", + "# y = b.flatten()\n", + "y = b\n", + "\n", + "reg = LinearRegression(fit_intercept=False).fit(X, y)\n", + "\n", + "# filename = './model/linearReg_ac_all_model.sav'\n", + "# pickle.dump(reg, open(filename, 'wb'))\n", + "# filename = './model/linearReg_ac_all_model.sav'\n", + "# outfilename = '../cache/db_ac_all/result_linearReg.csv'\n", + "# predict(filename,outfilename)\n", + "# pdb.set_trace()\n", + "predicted = reg.predict(X)\n", + "\n", + "plt.hist(reg.coef_[0][0:163], bins=50)\n", + "# plt.xscale('log')\n", + "plt.xlabel('$\\Delta_g G^o$')\n", + "plt.ylabel('Count')\n", + "# plt.savefig('./figures/linear_cc_groups.png')\n", + "\n", + "mse = mean_squared_error(y, predicted)\n", + "r2 = r2_score(y, predicted)\n", + "\n", + "print('Mean squared error: %.2f'\n", + " % mse)\n", + "# The coefficient of determination: 1 is perfect prediction\n", + "print('Coefficient of determination: %.4f'\n", + " % r2)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(y, predicted)\n", + "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=1)\n", + "ax.set_xlabel('Measured $\\Delta_r G^o$')\n", + "ax.set_ylabel('Predicted $\\Delta_r G^o$')\n", + "plt.figtext(.7, .2, \"MSE = %.2f\" % mse)\n", + "plt.figtext(.7, .25, \"$R^2$ = %.4f\" % r2)\n", + "# plt.savefig('./figures/linear_regression_cc.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cross validation group contribution " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "median of cv is: 5.484608039555587\n", + "mean of cv is: 118330593393.17863\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Cumulative distribution')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ac = loadmat('./data/component_contribution_python.mat')\n", + "\n", + "S = ac['train_S']\n", + "\n", + "df_S = pd.DataFrame(ac['train_S'])\n", + "df_S_unique = df_S.T.drop_duplicates().T\n", + "unque_cols = df_S_unique.columns.values.tolist()\n", + "S = S[:, unque_cols]\n", + "\n", + "G = ac['G']\n", + "\n", + "b_list = json.load(open('./data/median_b.json'))\n", + "b = np.asarray(b_list)\n", + "b = np.reshape(b,(-1,1))\n", + "\n", + "m, n = S.shape\n", + "assert G.shape[0] == m\n", + "assert b.shape == (n, 1)\n", + "\n", + "STG = np.dot(S.T,G)\n", + "\n", + "X = STG\n", + "y = b\n", + "\n", + "\n", + "# cross validation\n", + "regression = LinearRegression(fit_intercept=False)\n", + "# lasso = linear_model.Lasso()\n", + "\n", + "scores = -cross_val_score(regression, X, y, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", + "print('median of cv is: ', median(scores))\n", + "print('mean of cv is: ', mean(scores))\n", + "\n", + "\n", + "# print('std of cv is: ', scores.std)\n", + "x = np.sort(scores)\n", + "# y = np.arange(1,len(x)+1)/len(x)\n", + "y = 1. * np.arange(len(x)) / (len(x) - 1)\n", + "\n", + "fig = plt.figure(figsize=(6,6))\n", + "plt.xlim(right=15)\n", + "plt.plot(x,y,marker='.',linestyle='none')#,color=\"#273c75\")\n", + "plt.axhline(y=0.5,linewidth=1,color='grey')\n", + "plt.xlabel('|$\\Delta G^{\\'o}_{est} - \\Delta G^{\\'o}_{obs}$|')\n", + "plt.ylabel('Cumulative distribution')\n", + "# fig.savefig('./figures/cross_validation_cc.jpg')\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## M1-linear model regression " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean squared error: 38.30\n", + "Coefficient of determination: 0.9990\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.7, 0.25, '$R^2$ = 0.9990')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ac = loadmat('./data/dGPredictor_stereo.mat')\n", + "\n", + "S = ac['train_S']\n", + "\n", + "G = ac['G']\n", + "b = ac['b']\n", + "\n", + "\n", + "m, n = S.shape\n", + "assert G.shape[0] == m\n", + "assert b.shape == (n, 1)\n", + "\n", + "STG = np.dot(S.T,G)\n", + "\n", + "X = STG\n", + "# y = b.flatten()\n", + "y = b\n", + "\n", + "# reg = LinearRegression(fit_intercept=False).fit(X, y)\n", + "alphas = np.logspace(-6, 6, 200)\n", + "reg = RidgeCV(alphas=alphas, fit_intercept=False ).fit(X, y)\n", + "\n", + "plt.hist(reg.coef_[0][0:264], bins=50, color = 'burlywood')\n", + "# plt.xscale('log')\n", + "plt.xlabel('$\\Delta_g G^o$')\n", + "plt.ylabel('Count')\n", + "# plt.savefig('./figures/ridge_groups.png')\n", + "\n", + "predicted = reg.predict(X)\n", + "\n", + "mse = mean_squared_error(y, predicted)\n", + "r2 = r2_score(y, predicted)\n", + "\n", + "print('Mean squared error: %.2f'\n", + " % mse)\n", + "# The coefficient of determination: 1 is perfect prediction\n", + "print('Coefficient of determination: %.4f'\n", + " % r2)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(y, predicted, color = 'burlywood')\n", + "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=1,)\n", + "ax.set_xlabel('Measured $\\Delta_r G^o$')\n", + "ax.set_ylabel('Predicted $\\Delta_r G^o$')\n", + "plt.figtext(.7, .2, \"MSE = %.2f\" % mse)\n", + "plt.figtext(.7, .25, \"$R^2$ = %.4f\" % r2)\n", + "# plt.savefig('./figures/ridge_regression.png')\n", + "# plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## M1 linear model cross-validation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "median of cv is: 5.826162919031784\n", + "mean of cv is: 14.961333672834245\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Cumulative distribution')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ac = loadmat('./data/dGPredictor_stereo.mat')\n", + "\n", + "S = ac['train_S']\n", + "\n", + "df_S = pd.DataFrame(ac['train_S'])\n", + "df_S_unique = df_S.T.drop_duplicates().T\n", + "unque_cols = df_S_unique.columns.values.tolist()\n", + "S = S[:, unque_cols]\n", + "\n", + "G = ac['G']\n", + "\n", + "b_list = json.load(open('./data/median_b_extended.json'))\n", + "b = np.asarray(b_list)\n", + "b = np.reshape(b,(-1,1))\n", + "\n", + "m, n = S.shape\n", + "assert G.shape[0] == m\n", + "assert b.shape == (n, 1)\n", + "\n", + "STG = np.dot(S.T,G)\n", + "\n", + "X = STG\n", + "y = b\n", + "\n", + "alphas = np.logspace(-6, 6, 200)\n", + "\n", + "clf = RidgeCV(alphas=alphas, fit_intercept=False).fit(X, y)\n", + "# print(clf.alpha_)\n", + "clf_new = Ridge(alpha=clf.alpha_,fit_intercept=False)\n", + "\n", + "# y_pred = clf.predict(X)\n", + "scores = -cross_val_score(clf_new, X, y, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", + "\n", + "print('median of cv is: ', median(scores))\n", + "print('mean of cv is: ', mean(scores))\n", + "\n", + "x = np.sort(scores)\n", + "y = 1. * np.arange(len(x)) / (len(x) - 1)\n", + "\n", + "fig = plt.figure(figsize=(6,6))\n", + "plt.xlim(right=15)\n", + "plt.plot(x,y,marker='.',linestyle='none',color=\"burlywood\")\n", + "plt.axhline(y=0.5,linewidth=1,color='grey')\n", + "plt.xlabel('|$\\Delta G^{\\'o}_{est} - \\Delta G^{\\'o}_{obs}$|')\n", + "plt.ylabel('Cumulative distribution')\n", + "# fig.savefig('./figures/cross_validation_ridge.jpg')\n", + "# plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## M2-linearmodel regression analysis " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean squared error: 24.60\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.7, 0.25, '$R^2$ = 0.9994')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grp_inc = loadmat('./data/radius2_mat_data_modified_manual.mat')\n", + "\n", + "b = grp_inc['b']\n", + "G = grp_inc['G_inc_r2_compar'] #group incidence matrix for training data\n", + "X = grp_inc['X_train']\n", + "\n", + "\n", + "# G = grp_inc['G_inc'] #group incidence matrix for KEGG data \n", + "# X = grp_inc['X_all']\n", + "\n", + "y = b\n", + "\n", + "alphas = np.logspace(-6, 6, 200)\n", + "reg = RidgeCV(alphas=alphas, fit_intercept=False ).fit(X, y)\n", + "\n", + "\n", + "plt.hist(reg.coef_[0][0:1420], bins=50, color = 'tomato')#for ridgeCV\n", + "\n", + "\n", + "plt.xlabel('$\\Delta_g G^o$')\n", + "plt.ylabel('Count')\n", + "# plt.savefig('./figures/ridge_group_info_radius2_manual_correct_new_color.png')\n", + "\n", + "predicted= reg.predict(X)\n", + "\n", + "mse = mean_squared_error(y, predicted)\n", + "r2 = r2_score(y, predicted)\n", + "\n", + "print('Mean squared error: %.2f'\n", + " % mse)\n", + "# The coefficient of determination: 1 is perfect prediction\n", + "# print('Coefficient of determination: %.4f'\n", + "# % r2)\n", + "\n", + "fig, ax = plt.subplots()\n", + "# ax.scatter(y, predicted, color = 'burlywood')\n", + "ax.scatter(y, predicted, color = 'tomato')\n", + "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=1,)\n", + "ax.set_xlabel('Measured $\\Delta_r G^o$')\n", + "ax.set_ylabel('Predicted $\\Delta_r G^o$')\n", + "plt.figtext(.7, .2, \"MSE = %.2f\" % mse)\n", + "plt.figtext(.7, .25, \"$R^2$ = %.4f\" % r2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## M2-linear cross-validation " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.46595256686646774\n", + "median of cv is: 15.459406503468742\n", + "mean of cv is: 35.963420987067806\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Cumulative distribution')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ac = loadmat('./data/radius2_mat_data_modified_manual.mat')\n", + "S = ac['train_S']\n", + "\n", + "G = grp_inc['G_inc_r2_compar']\n", + "\n", + "df_S = pd.DataFrame(ac['train_S'])\n", + "df_S_unique = df_S.T.drop_duplicates().T\n", + "unque_cols = df_S_unique.columns.values.tolist()\n", + "S = S[:, unque_cols]\n", + "\n", + "\n", + "b_list = json.load(open('./data/median_b_manual_correction_r2.json'))\n", + "b = np.asarray(b_list)\n", + "b = np.reshape(b,(-1,1))\n", + "\n", + "m, n = S.shape\n", + "assert G.shape[0] == m\n", + "assert b.shape == (n, 1)\n", + "\n", + "STG = np.dot(S.T,G)\n", + "\n", + "X = STG\n", + "y = b\n", + "\n", + "alphas = np.logspace(-6, 6, 200)\n", + "\n", + "clf = RidgeCV(alphas=alphas, fit_intercept=False).fit(X, y)\n", + "print(clf.alpha_)\n", + "clf_new = Ridge(alpha=clf.alpha_,fit_intercept=False)\n", + "\n", + "scores = -cross_val_score(clf_new, X, y, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", + "print('median of cv is: ', median(scores))\n", + "print('mean of cv is: ', mean(scores))\n", + "\n", + "x = np.sort(scores)\n", + "y = 1. * np.arange(len(x)) / (len(x) - 1)\n", + "\n", + "fig = plt.figure(figsize=(6,6))\n", + "plt.xlim(right=40)\n", + "# plt.plot(x,y,marker='.',linestyle='none',color=\"burlywood\")\n", + "plt.plot(x,y,marker='.',linestyle='none',color=\"tomato\")\n", + "plt.axhline(y=0.5,linewidth=1,color='grey')\n", + "plt.xlabel('|$\\Delta G^{\\'o}_{est} - \\Delta G^{\\'o}_{obs}$|')\n", + "plt.ylabel('Cumulative distribution')\n", + "# fig.savefig('./figures/cross_validation_ridge_radius2.jpg')\n", + "# plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## M-1,2-Linear model regression " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "radius 1+2 linear model\n", + "Mean squared error: 9.60\n", + "Coefficient of determination: 0.9998\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.7, 0.25, '$R^2$ = 0.9998')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ac = loadmat('./data/Train_comb_NN_model.mat')\n", + "\n", + "y = ac['y']\n", + "y = y.flatten()\n", + "\n", + "alphas = np.logspace(-6, 6, 200)\n", + "\n", + "\n", + "Xrc = ac['X_comb_train']\n", + "regr_rcombined = RidgeCV(alphas=alphas,fit_intercept= False).fit(Xrc, y)\n", + "\n", + "y_pred_rc = regr_rcombined.predict(Xrc)\n", + "mse_rc = mean_squared_error(y, y_pred_rc)\n", + "r2 = r2_score(y, y_pred_rc)\n", + "\n", + "\n", + "print('radius 1+2 linear model')\n", + "print('Mean squared error: %.2f'\n", + " % mse_rc)\n", + "print('Coefficient of determination: %.4f'\n", + " % r2)\n", + "\n", + "plt.hist(regr_rcombined.coef_[0:1730], bins=50, color = 'tomato')#for ridgeCV\n", + "\n", + "plt.xlabel('$\\Delta_g G^o$')\n", + "plt.ylabel('Count')\n", + "# plt.savefig('./figures/ridge_group_info_radius2_manual_correct_new_color.png')\n", + "\n", + "fig, ax = plt.subplots()\n", + "# ax.scatter(y, predicted, color = 'burlywood')\n", + "ax.scatter(y, y_pred_rc, color = 'tomato')\n", + "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=1,)\n", + "ax.set_xlabel('Measured $\\Delta_r G^o$')\n", + "ax.set_ylabel('Predicted $\\Delta_r G^o$')\n", + "plt.figtext(.7, .2, \"MSE = %.2f\" % mse_rc)\n", + "plt.figtext(.7, .25, \"$R^2$ = %.4f\" % r2)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Leave one out cross-validation for M-1,2-linear model " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cross-validataion result : radius 1 + 2\n", + "median of cv is: 5.484989593134893\n", + "mean of cv is: 16.256106507029465\n" + ] + } + ], + "source": [ + "r1_d = loadmat('./data/dGPredictor_stereo.mat')\n", + "r2_d = loadmat('./data/radius2_mat_data_modified_manual.mat')\n", + "S = r1_d['train_S']\n", + "\n", + "Gr1 = r1_d['G']\n", + "Gr2 = r2_d['G_inc_r2_compar']\n", + "\n", + "df_S = pd.DataFrame(r1_d['train_S'])\n", + "df_S_unique = df_S.T.drop_duplicates().T\n", + "unque_cols = df_S_unique.columns.values.tolist()\n", + "S = S[:, unque_cols]\n", + "\n", + "b_list = json.load(open('./data/median_b_manual_correction_r2.json')) # it will be same for both radius, it just remove all the repeated data points from the training data\n", + "b = np.asarray(b_list)\n", + "b = np.reshape(b,(-1,1))\n", + "\n", + "STG1 = np.dot(S.T, Gr1)\n", + "STG2 = np.dot(S.T, Gr2)\n", + "\n", + "\n", + "X1 = STG1\n", + "X2 = STG2\n", + "yy = b\n", + "yy = yy.flatten()\n", + "\n", + "\n", + "## cross validation combined moiety model\n", + "\n", + "XX = np.concatenate((X1, X2), axis =1)\n", + "\n", + "alphas = np.logspace(-6, 6, 200)\n", + "regr = RidgeCV(alphas=alphas,fit_intercept= False).fit(XX, yy)\n", + "\n", + "regr_cv = Ridge(alpha=regr.alpha_,fit_intercept=False)\n", + "scores_cv = -cross_val_score(regr_cv, XX, yy, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", + "\n", + "\n", + "print('cross-validataion result : radius 1 + 2')\n", + "print('median of cv is: ', median(scores_cv))\n", + "print('mean of cv is: ', mean(scores_cv))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## regression M1, M2, M1-2 non linear model " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "radius 1 non-linear model\n", + "Mean squared error: 20.91\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andrew Freiburger\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:536: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n" + ] + } + ], + "source": [ + "ac = loadmat('./data/Train_comb_NN_model.mat')\n", + "\n", + "# M1 non-linear model \n", + "\n", + "y = ac['y']\n", + "y = y.flatten()\n", + "\n", + "Xr1 = ac['X_r1_train']\n", + "max_i = 1000\n", + "regrr1 = MLPRegressor(solver = 'lbfgs', max_iter = max_i).fit(Xr1, y)\n", + "y_predr1 = regrr1.predict(Xr1)\n", + "mser1 = mean_squared_error(y, y_predr1)\n", + "\n", + "print('radius 1 non-linear model')\n", + "print('Mean squared error: %.2f'\n", + " % mser1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "radius 2 non-linear model\n", + "Mean squared error: 6.91\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andrew Freiburger\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:536: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n" + ] + } + ], + "source": [ + "# M2 non-linear model \n", + "\n", + "Xr2 = ac['X_r2_train']\n", + "regrr2 = MLPRegressor(solver = 'lbfgs', max_iter = max_i).fit(Xr2, y)\n", + "y_predr2 = regrr2.predict(Xr2)\n", + "mser2 = mean_squared_error(y, y_predr2)\n", + "\n", + "print('radius 2 non-linear model')\n", + "print('Mean squared error: %.2f'\n", + " % mser2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "radius 1+2 non-linear model\n", + "Mean squared error: 6.92\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Andrew Freiburger\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:536: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n" + ] + } + ], + "source": [ + "## M1-2 non-linear model \n", + "\n", + "Xrc = ac['X_comb_train']\n", + "regr_rcombined = MLPRegressor(solver = 'lbfgs', max_iter = max_i).fit(Xrc, y)\n", + "y_pred_rc = regr_rcombined.predict(Xrc)\n", + "mse_rc = mean_squared_error(y, y_pred_rc)\n", + "\n", + "print('radius 1+2 non-linear model')\n", + "print('Mean squared error: %.2f'\n", + " % mse_rc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## cross-validation NN models" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cross-validataion result : radius 1\n", + "median of cv is: 7.348531929169212\n", + "mean of cv is: 17.474064815854383\n", + "cross-validataion result : radius 2\n", + "median of cv is: 13.967952708932387\n", + "mean of cv is: 35.2431183450809\n", + "cross-validataion result : radius 1 + 2\n", + "median of cv is: 7.194199873332677\n", + "mean of cv is: 18.58908445912685\n" + ] + } + ], + "source": [ + "r1_d = loadmat('./data/dGPredictor_stereo.mat')\n", + "r2_d = loadmat('./data/radius2_mat_data_modified_manual.mat')\n", + "S = r1_d['train_S']\n", + "\n", + "Gr1 = r1_d['G']\n", + "Gr2 = r2_d['G_inc_r2_compar']\n", + "\n", + "df_S = pd.DataFrame(r1_d['train_S'])\n", + "df_S_unique = df_S.T.drop_duplicates().T\n", + "unque_cols = df_S_unique.columns.values.tolist()\n", + "S = S[:, unque_cols]\n", + "\n", + "b_list = json.load(open('./data/median_b_manual_correction_r2.json')) # it will be same for both radius, it just remove all the repeated data points from the training data\n", + "b = np.asarray(b_list)\n", + "b = np.reshape(b,(-1,1))\n", + "\n", + "STG1 = np.dot(S.T, Gr1)\n", + "STG2 = np.dot(S.T, Gr2)\n", + "\n", + "\n", + "X1 = STG1\n", + "X2 = STG2\n", + "yy = b\n", + "yy = yy.flatten()\n", + "\n", + "## cross validation r =1\n", + "regr_cvr1 = MLPRegressor(solver = 'lbfgs', max_iter = 10000).fit(X1, yy)\n", + "\n", + "scores_cv1 = -cross_val_score(regr_cvr1, X1, yy, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", + "\n", + "print('cross-validataion result : radius 1')\n", + "print('median of cv is: ', median(scores_cv1))\n", + "print('mean of cv is: ', mean(scores_cv1))\n", + "\n", + "\n", + "## cross validation r =2 \n", + "regr_cvr2 = MLPRegressor(solver = 'lbfgs', max_iter = 10000).fit(X2, yy)\n", + "\n", + "scores_cv2 = -cross_val_score(regr_cvr2, X2, yy, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", + "\n", + "print('cross-validataion result : radius 2')\n", + "print('median of cv is: ', median(scores_cv2))\n", + "print('mean of cv is: ', mean(scores_cv2))\n", + "\n", + "\n", + "## cross validation combined moiety model\n", + "\n", + "XX = np.concatenate((X1, X2), axis =1)\n", + "\n", + "regr_cv = MLPRegressor(solver = 'lbfgs', max_iter = 10000).fit(XX, yy)\n", + "scores_cv = -cross_val_score(regr_cv, XX, yy, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", + "\n", + "print('cross-validataion result : radius 1 + 2')\n", + "print('median of cv is: ', median(scores_cv))\n", + "print('mean of cv is: ', mean(scores_cv))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/retrieve_bulk-checkpoint.ipynb b/.ipynb_checkpoints/retrieve_bulk-checkpoint.ipynb new file mode 100644 index 0000000..0fc2484 --- /dev/null +++ b/.ipynb_checkpoints/retrieve_bulk-checkpoint.ipynb @@ -0,0 +1,660 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-08-13 17:29:46.477 INFO rdkit: Enabling RDKit 2021.03.4 jupyter extensions\n" + ] + } + ], + "source": [ + "import streamlit as st\n", + "import pandas as pd\n", + "import numpy as np\n", + "import re\n", + "from PIL import Image\n", + "import webbrowser\n", + "import json\n", + "import pickle\n", + "import sys \n", + "import joblib\n", + "\n", + "sys.path.append('./CC/')\n", + "\n", + "import chemaxon\n", + "from chemaxon import *\n", + "from compound import Compound\n", + "from compound_cacher import CompoundCacher\n", + "from rdkit.Chem import rdChemReactions as Reactions\n", + "from rdkit.Chem import Draw\n", + "from rdkit import Chem" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load_smiles():\n", + " db = pd.read_csv('./data/cache_compounds_20160818.csv',\n", + " index_col='compound_id')\n", + " db_smiles = db['smiles_pH7'].to_dict()\n", + " return db_smiles\n", + "\n", + "def load_molsig_rad1():\n", + " molecular_signature_r1 = json.load(open('./data/decompose_vector_ac.json'))\n", + " return molecular_signature_r1\n", + "\n", + "\n", + "def load_molsig_rad2():\n", + " molecular_signature_r2 = json.load(\n", + " open('./data/decompose_vector_ac_r2_py3_indent_modified_manual.json'))\n", + " return molecular_signature_r2\n", + "\n", + "\n", + "def load_model():\n", + " filename = './model/M12_model_BR.pkl'\n", + " loaded_model = joblib.load(open(filename, 'rb'))\n", + " return loaded_model\n", + "\n", + "\n", + "def load_compound_cache():\n", + " ccache = CompoundCacher()\n", + " return ccache\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def count_substructures(radius, molecule):\n", + " \"\"\"Helper function for get the information of molecular signature of a\n", + " metabolite. The relaxed signature requires the number of each substructure\n", + " to construct a matrix for each molecule.\n", + " Parameters\n", + " ----------\n", + " radius : int\n", + " the radius is bond-distance that defines how many neighbor atoms should\n", + " be considered in a reaction center.\n", + " molecule : Molecule\n", + " a molecule object create by RDkit (e.g. Chem.MolFromInchi(inchi_code)\n", + " or Chem.MolToSmiles(smiles_code))\n", + " Returns\n", + " -------\n", + " dict\n", + " dictionary of molecular signature for a molecule,\n", + " {smiles: molecular_signature}\n", + " \"\"\"\n", + " m = molecule\n", + " smi_count = dict()\n", + " atomList = [atom for atom in m.GetAtoms()]\n", + "\n", + " for i in range(len(atomList)):\n", + " env = Chem.FindAtomEnvironmentOfRadiusN(m, radius, i)\n", + " atoms = set()\n", + " for bidx in env:\n", + " atoms.add(m.GetBondWithIdx(bidx).GetBeginAtomIdx())\n", + " atoms.add(m.GetBondWithIdx(bidx).GetEndAtomIdx())\n", + "\n", + " # only one atom is in this environment, such as O in H2O\n", + " if len(atoms) == 0:\n", + " atoms = {i}\n", + "\n", + " smi = Chem.MolFragmentToSmiles(m, atomsToUse=list(atoms),\n", + " bondsToUse=env, canonical=True)\n", + "\n", + " if smi in smi_count:\n", + " smi_count[smi] = smi_count[smi] + 1\n", + " else:\n", + " smi_count[smi] = 1\n", + " return smi_count\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def decompse_novel_mets_rad1(novel_smiles, radius=1):\n", + " decompose_vector = dict()\n", + "\n", + " for cid, smiles_pH7 in novel_smiles.items():\n", + " mol = Chem.MolFromSmiles(smiles_pH7)\n", + " mol = Chem.RemoveHs(mol)\n", + " # Chem.RemoveStereochemistry(mol)\n", + " smi_count = count_substructures(radius, mol)\n", + " decompose_vector[cid] = smi_count\n", + " return decompose_vector\n", + "\n", + "\n", + "def decompse_novel_mets_rad2(novel_smiles, radius=2):\n", + " decompose_vector = dict()\n", + "\n", + " for cid, smiles_pH7 in novel_smiles.items():\n", + " mol = Chem.MolFromSmiles(smiles_pH7)\n", + " mol = Chem.RemoveHs(mol)\n", + " # Chem.RemoveStereochemistry(mol)\n", + " smi_count = count_substructures(radius, mol)\n", + " decompose_vector[cid] = smi_count\n", + " return decompose_vector\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def parse_reaction_formula_side(s):\n", + " \"\"\"\n", + " Parses the side formula, e.g. '2 C00001 + C00002 + 3 C00003'\n", + " Ignores stoichiometry.\n", + "\n", + " Returns:\n", + " The set of CIDs.\n", + " \"\"\"\n", + " if s.strip() == \"null\":\n", + " return {}\n", + "\n", + " compound_bag = {}\n", + " for member in re.split('\\s+\\+\\s+', s):\n", + " tokens = member.split(None, 1)\n", + " if len(tokens) == 0:\n", + " continue\n", + " if len(tokens) == 1:\n", + " amount = 1\n", + " key = member\n", + " else:\n", + " amount = float(tokens[0])\n", + " key = tokens[1]\n", + "\n", + " compound_bag[key] = compound_bag.get(key, 0) + amount\n", + "\n", + " return compound_bag\n", + "\n", + "\n", + "def parse_formula(formula, arrow='<=>', rid=None):\n", + " \"\"\"\n", + " Parses a two-sided formula such as: 2 C00001 => C00002 + C00003\n", + "\n", + " Return:\n", + " The set of substrates, products and the direction of the reaction\n", + " \"\"\"\n", + " tokens = formula.split(arrow)\n", + " if len(tokens) < 2:\n", + " print(('Reaction does not contain the arrow sign (%s): %s'\n", + " % (arrow, formula)))\n", + " if len(tokens) > 2:\n", + " print(('Reaction contains more than one arrow sign (%s): %s'\n", + " % (arrow, formula)))\n", + "\n", + " left = tokens[0].strip()\n", + " right = tokens[1].strip()\n", + "\n", + " sparse_reaction = {}\n", + " for cid, count in parse_reaction_formula_side(left).items():\n", + " sparse_reaction[cid] = sparse_reaction.get(cid, 0) - count\n", + "\n", + " for cid, count in parse_reaction_formula_side(right).items():\n", + " sparse_reaction[cid] = sparse_reaction.get(cid, 0) + count\n", + "\n", + " return sparse_reaction\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def draw_rxn_figure(rxn_dict, db_smiles, novel_smiles):\n", + " # db_smiles = load_smiles()\n", + "\n", + " left = ''\n", + " right = ''\n", + "\n", + " for met, stoic in rxn_dict.items():\n", + " if met == \"C00080\" or met == \"C00282\":\n", + " continue # hydogen is not considered\n", + " if stoic > 0:\n", + " if met in db_smiles:\n", + " right = right + db_smiles[met] + '.'\n", + " else:\n", + " right = right + novel_smiles[met] + '.'\n", + " else:\n", + " if met in db_smiles:\n", + " left = left + db_smiles[met] + '.'\n", + " else:\n", + " left = left + novel_smiles[met] + '.'\n", + " smarts = left[:-1] + '>>' + right[:-1]\n", + " # print smarts\n", + " smarts = str(smarts)\n", + " rxn = Reactions.ReactionFromSmarts(smarts, useSmiles=True)\n", + " return Draw.ReactionToImage(rxn) # , subImgSize=(400, 400))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def get_rule(rxn_dict, molsig1, molsig2, novel_decomposed1, novel_decomposed2):\n", + " if novel_decomposed1 != None:\n", + " for cid in novel_decomposed1:\n", + " molsig1[cid] = novel_decomposed1[cid]\n", + " if novel_decomposed2 != None:\n", + " for cid in novel_decomposed2:\n", + " molsig2[cid] = novel_decomposed2[cid]\n", + "\n", + " molsigna_df1 = pd.DataFrame.from_dict(molsig1).fillna(0)\n", + " all_mets1 = molsigna_df1.columns.tolist()\n", + " all_mets1.append(\"C00080\")\n", + " all_mets1.append(\"C00282\")\n", + "\n", + " molsigna_df2 = pd.DataFrame.from_dict(molsig2).fillna(0)\n", + " all_mets2 = molsigna_df2.columns.tolist()\n", + " all_mets2.append(\"C00080\")\n", + " all_mets2.append(\"C00282\")\n", + "\n", + " moieties_r1 = open('./data/group_names_r1.txt')\n", + " moieties_r2 = open('./data/group_names_r2_py3_modified_manual.txt')\n", + " moie_r1 = moieties_r1.read().splitlines()\n", + " moie_r2 = moieties_r2.read().splitlines()\n", + "\n", + " molsigna_df1 = molsigna_df1.reindex(moie_r1)\n", + " molsigna_df2 = molsigna_df2.reindex(moie_r2)\n", + "\n", + " rule_df1 = pd.DataFrame(index=molsigna_df1.index)\n", + " rule_df2 = pd.DataFrame(index=molsigna_df2.index)\n", + " # for rid, value in reaction_dict.items():\n", + " # # skip the reactions with missing metabolites\n", + " # mets = value.keys()\n", + " # flag = False\n", + " # for met in mets:\n", + " # if met not in all_mets:\n", + " # flag = True\n", + " # break\n", + " # if flag: continue\n", + "\n", + " rule_df1['change'] = 0\n", + " for met, stoic in rxn_dict.items():\n", + " if met == \"C00080\" or met == \"C00282\":\n", + " continue # hydogen is zero\n", + " rule_df1['change'] += molsigna_df1[met] * stoic\n", + "\n", + " rule_df2['change'] = 0\n", + " for met, stoic in rxn_dict.items():\n", + " if met == \"C00080\" or met == \"C00282\":\n", + " continue # hydogen is zero\n", + " rule_df2['change'] += molsigna_df2[met] * stoic\n", + "\n", + " rule_vec1 = rule_df1.to_numpy().T\n", + " rule_vec2 = rule_df2.to_numpy().T\n", + "\n", + " m1, n1 = rule_vec1.shape\n", + " m2, n2 = rule_vec2.shape\n", + "\n", + " zeros1 = np.zeros((m1, 44))\n", + " zeros2 = np.zeros((m2, 44))\n", + " X1 = np.concatenate((rule_vec1, zeros1), 1)\n", + " X2 = np.concatenate((rule_vec2, zeros2), 1)\n", + "\n", + " rule_comb = np.concatenate((X1, X2), 1)\n", + "\n", + " # rule_df_final = {}\n", + " # rule_df_final['rad1'] = rule_df1\n", + " # rule_df_final['rad2'] = rule_df2\n", + " return rule_comb, rule_df1, rule_df2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def get_ddG0(rxn_dict, pH, I, novel_mets):\n", + " ccache = CompoundCacher()\n", + " # ddG0 = get_transform_ddG0(rxn_dict, ccache, pH, I, T)\n", + " T = 298.15\n", + " ddG0_forward = 0\n", + " for compound_id, coeff in rxn_dict.items():\n", + " if novel_mets != None and compound_id in novel_mets:\n", + " comp = novel_mets[compound_id]\n", + " else:\n", + " comp = ccache.get_compound(compound_id)\n", + " ddG0_forward += coeff * comp.transform_pH7(pH, I, T)\n", + "\n", + " return ddG0_forward" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def get_dG0(rxn_dict, rid, pH, I, loaded_model, molsig_r1, molsig_r2, novel_decomposed_r1, novel_decomposed_r2, novel_mets):\n", + "\n", + " # rule_df = get_rxn_rule(rid)\n", + " rule_comb, rule_df1, rule_df2 = get_rule(\n", + " rxn_dict, molsig_r1, molsig_r2, novel_decomposed_r1, novel_decomposed_r2)\n", + "\n", + " X = rule_comb\n", + "\n", + " ymean, ystd = loaded_model.predict(X, return_std=True)\n", + "\n", + " result = {}\n", + " # result['dG0'] = ymean[0] + get_ddG0(rxn_dict, pH, I)\n", + " # result['standard deviation'] = ystd[0]\n", + "\n", + " # result_df = pd.DataFrame([result])\n", + " # result_df.style.hide_index()\n", + " # return result_df\n", + " return ymean[0] + get_ddG0(rxn_dict, pH, I, novel_mets), ystd[0], rule_df1, rule_df2\n", + " # return ymean[0],ystd[0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def parse_novel_molecule(add_info):\n", + " result = {}\n", + " for cid, InChI in add_info.items():\n", + " c = Compound.from_inchi('Test', cid, InChI)\n", + " result[cid] = c\n", + " return result\n", + "\n", + "\n", + "def parse_novel_smiles(result):\n", + " novel_smiles = {}\n", + " for cid, c in result.items():\n", + " smiles = c.smiles_pH7\n", + " novel_smiles[cid] = smiles\n", + " return novel_smiles\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "db_smiles = load_smiles()\n", + "molsig_r1 = load_molsig_rad1()\n", + "molsig_r2 = load_molsig_rad2()\n", + "\n", + "loaded_model = load_model()\n", + "ccache = load_compound_cache()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimating dG for reaction with novel metabolite" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "rxn_str = 'C01745 + C00004 <=> N00001 + C00003 + C00001'" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'C01745 + C00004 <=> N00001 + C00003 + C00001'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rxn_str" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "add_info = {\"N00001\":\"InChI=1S/C14H12O/c15-14-8-4-7-13(11-14)10-9-12-5-2-1-3-6-12/h1-11,15H/b10-9+\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'InChI=1S/C14H12O/c15-14-8-4-7-13(11-14)10-9-12-5-2-1-3-6-12/h1-11,15H/b10-9+'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "add_info['N00001']" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "pH = 7 # any number between 0-14 \n", + "I = 0.1 #min_value=0.0, max_value=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'N00001': 'Oc1cccc(/C=C/c2ccccc2)c1'}\n" + ] + } + ], + "source": [ + "try:\n", + " novel_mets = parse_novel_molecule(add_info)\n", + " novel_smiles = parse_novel_smiles(novel_mets)\n", + " novel_decomposed_r1 = decompse_novel_mets_rad1(novel_smiles)\n", + " novel_decomposed_r2 = decompse_novel_mets_rad2(novel_smiles)\n", + "\n", + "except Exception as e:\n", + " novel_mets = None\n", + " novel_smiles = None\n", + " novel_decomposed_r1 = None\n", + " novel_decomposed_r2 = None\n", + "\n", + "print(novel_smiles)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "rxn_dict = parse_formula(rxn_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "draw_rxn_figure(rxn_dict, db_smiles,novel_smiles)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dG = -121.79 ± 100.57 kJ/mol\n" + ] + } + ], + "source": [ + "mu, std, rule_df1, rule_df2 = get_dG0(rxn_dict, 'R00801', pH, I, loaded_model, molsig_r1, molsig_r2, novel_decomposed_r1, novel_decomposed_r2, novel_mets)\n", + "\n", + "print(\"dG = %.2f ± %.2f kJ/mol\" % (mu, std))\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bulk estimation of dG for a list of KEGG reactions" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "KEGG_rxn_list = {\"R00010\" : \"C01083 + C00001 <=> 2 C00031\",\n", + " \"R00303\" : \"C00092 + C00001 <=> C00031 + C00009\",\n", + " \"R00304\" : \"C00103 + C00001 <=> C00031 + C00009\",\n", + " \"R07294\" : \"C15524 + C00001 <=> C02137 + C00010\",\n", + " \"R01252\" : \"C00148 + C00026 + C00007 <=> C01157 + C00042 + C00011\",\n", + " \"R00406\" : \"C00091 + C00149 <=> C00042 + C04348\"\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R00010\n", + "C01083 + C00001 <=> 2 C00031\n", + "dG = -12.45 ± 3.49 kJ/mol\n", + "R00303\n", + "C00092 + C00001 <=> C00031 + C00009\n", + "dG = -12.40 ± 3.30 kJ/mol\n", + "R00304\n", + "C00103 + C00001 <=> C00031 + C00009\n", + "dG = -18.78 ± 3.37 kJ/mol\n", + "R07294\n", + "C15524 + C00001 <=> C02137 + C00010\n", + "dG = -14.46 ± 31.43 kJ/mol\n", + "R01252\n", + "C00148 + C00026 + C00007 <=> C01157 + C00042 + C00011\n", + "dG = -427.04 ± 41.12 kJ/mol\n", + "R00406\n", + "C00091 + C00149 <=> C00042 + C04348\n", + "dG = -3.27 ± 4.37 kJ/mol\n" + ] + } + ], + "source": [ + "pH = 7 # any number between 0-14 \n", + "I = 0.1 #min_value=0.0, max_value=0.5)\n", + "\n", + "for keys in KEGG_rxn_list:\n", + " kegg_rxn_string = KEGG_rxn_list[keys]\n", + " kegg_rxn_dict = parse_formula(kegg_rxn_string)\n", + " mu, std, rule_df1, rule_df2 = get_dG0(kegg_rxn_dict, keys, pH, I, loaded_model, molsig_r1, molsig_r2, [], [], [])\n", + " print(keys)\n", + " print(kegg_rxn_string)\n", + " print(\"dG = %.2f ± %.2f kJ/mol\" % (mu, std))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/CC/.ipynb_checkpoints/chemaxon-checkpoint.py b/CC/.ipynb_checkpoints/chemaxon-checkpoint.py index df41a15..7f52469 100644 --- a/CC/.ipynb_checkpoints/chemaxon-checkpoint.py +++ b/CC/.ipynb_checkpoints/chemaxon-checkpoint.py @@ -9,7 +9,7 @@ from rdkit.Chem import rdchem if platform.system() == 'Windows': - CXCALC_BIN = 'C:\\Users\\vuu10\\AppData\\Local\\Programs\\ChemAxon\\MarvinSuite\\bin\\cxcalc.exe' + CXCALC_BIN = 'C:\\Program Files\\ChemAxon\\MarvinSuite\\bin\\cxcalc.exe' #CXCALC_BIN = 'C:\\Program Files (x86)\\ChemAxon\\MarvinBeans\\bin\\cxcalc.bat' use_shell_for_echo = True else: diff --git a/CC/__pycache__/chemaxon.cpython-39.pyc b/CC/__pycache__/chemaxon.cpython-39.pyc new file mode 100644 index 0000000..34149f9 Binary files /dev/null and b/CC/__pycache__/chemaxon.cpython-39.pyc differ diff --git a/CC/__pycache__/compound.cpython-39.pyc b/CC/__pycache__/compound.cpython-39.pyc new file mode 100644 index 0000000..a4b1e27 Binary files /dev/null and b/CC/__pycache__/compound.cpython-39.pyc differ diff --git a/CC/__pycache__/compound_cacher.cpython-39.pyc b/CC/__pycache__/compound_cacher.cpython-39.pyc new file mode 100644 index 0000000..6c95612 Binary files /dev/null and b/CC/__pycache__/compound_cacher.cpython-39.pyc differ diff --git a/CC/__pycache__/thermodynamic_constants.cpython-39.pyc b/CC/__pycache__/thermodynamic_constants.cpython-39.pyc new file mode 100644 index 0000000..7e7e818 Binary files /dev/null and b/CC/__pycache__/thermodynamic_constants.cpython-39.pyc differ diff --git a/CC/chemaxon.py b/CC/chemaxon.py index df41a15..7f52469 100644 --- a/CC/chemaxon.py +++ b/CC/chemaxon.py @@ -9,7 +9,7 @@ from rdkit.Chem import rdchem if platform.system() == 'Windows': - CXCALC_BIN = 'C:\\Users\\vuu10\\AppData\\Local\\Programs\\ChemAxon\\MarvinSuite\\bin\\cxcalc.exe' + CXCALC_BIN = 'C:\\Program Files\\ChemAxon\\MarvinSuite\\bin\\cxcalc.exe' #CXCALC_BIN = 'C:\\Program Files (x86)\\ChemAxon\\MarvinBeans\\bin\\cxcalc.bat' use_shell_for_echo = True else: diff --git a/README.md b/README.md index 67829b8..e3859bb 100644 --- a/README.md +++ b/README.md @@ -30,6 +30,7 @@ Recommended- - use command "pip install -U streamlit" 7. Openbabel - run "conda install -c conda-forge openbabel" +- Installation via "pip" may require a wheel binary from this site https://www.lfd.uci.edu/~gohlke/pythonlibs/#openbabel, since the PyPI version may yield a "SWIG failed" installation error. 8. ChemAxon's Marvin (PkA value estimation) - Marvin is only required for adding structures of novel metabolites/compounds that are not in the KEGG database - instructions (https://chemaxon.com/products/marvin/download) diff --git a/analysis_dGPredictor.ipynb b/analysis_dGPredictor.ipynb index bc181fc..71d22f5 100644 --- a/analysis_dGPredictor.ipynb +++ b/analysis_dGPredictor.ipynb @@ -52,26 +52,22 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -143,8 +139,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "median of cv is: 5.460625755099045\n", - "mean of cv is: 188754266931.4596\n" + "median of cv is: 5.484608039555587\n", + "mean of cv is: 118330593393.17863\n" ] }, { @@ -159,14 +155,12 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -343,8 +333,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "median of cv is: 5.82616291903174\n", - "mean of cv is: 14.961333672834286\n" + "median of cv is: 5.826162919031784\n", + "mean of cv is: 14.961333672834245\n" ] }, { @@ -359,14 +349,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -537,7 +521,7 @@ "text": [ "0.46595256686646774\n", "median of cv is: 15.459406503468742\n", - "mean of cv is: 35.96342098706781\n" + "mean of cv is: 35.963420987067806\n" ] }, { @@ -552,14 +536,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -621,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -639,32 +621,28 @@ "Text(0.7, 0.25, '$R^2$ = 0.9998')" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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\n"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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -718,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -726,8 +704,8 @@ "output_type": "stream", "text": [ "cross-validataion result : radius 1 + 2\n", - "median of cv is: 5.484989593126512\n", - "mean of cv is: 16.256106507029173\n" + "median of cv is: 5.484989593134893\n", + "mean of cv is: 16.256106507029465\n" ] } ], @@ -783,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -791,14 +769,14 @@ "output_type": "stream", "text": [ "radius 1 non-linear model\n", - "Mean squared error: 20.85\n" + "Mean squared error: 20.91\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "C:\\Users\\Andrew Freiburger\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:536: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -828,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -843,7 +821,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "C:\\Users\\Andrew Freiburger\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:536: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -867,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -882,7 +860,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "C:\\Users\\Andrew Freiburger\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:536: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -913,73 +891,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 13, + "metadata": { + "scrolled": true, + "tags": [] + }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n", - "C:\\Users\\vuu10\\AppData\\Local\\Continuum\\anaconda3\\envs\\dGPredictor_py3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - " self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n" + "cross-validataion result : radius 1\n", + "median of cv is: 7.348531929169212\n", + "mean of cv is: 17.474064815854383\n", + "cross-validataion result : radius 2\n", + "median of cv is: 13.967952708932387\n", + "mean of cv is: 35.2431183450809\n", + "cross-validataion result : radius 1 + 2\n", + "median of cv is: 7.194199873332677\n", + "mean of cv is: 18.58908445912685\n" ] } ], @@ -1010,7 +940,7 @@ "yy = yy.flatten()\n", "\n", "## cross validation r =1\n", - "regr_cvr1 = MLPRegressor(solver = 'lbfgs', max_iter = 1000).fit(X1, yy)\n", + "regr_cvr1 = MLPRegressor(solver = 'lbfgs', max_iter = 10000).fit(X1, yy)\n", "\n", "scores_cv1 = -cross_val_score(regr_cvr1, X1, yy, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", "\n", @@ -1020,7 +950,7 @@ "\n", "\n", "## cross validation r =2 \n", - "regr_cvr2 = MLPRegressor(solver = 'lbfgs', max_iter = 1000).fit(X2, yy)\n", + "regr_cvr2 = MLPRegressor(solver = 'lbfgs', max_iter = 10000).fit(X2, yy)\n", "\n", "scores_cv2 = -cross_val_score(regr_cvr2, X2, yy, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", "\n", @@ -1033,7 +963,7 @@ "\n", "XX = np.concatenate((X1, X2), axis =1)\n", "\n", - "regr_cv = MLPRegressor(solver = 'lbfgs', max_iter = 1000).fit(XX, yy)\n", + "regr_cv = MLPRegressor(solver = 'lbfgs', max_iter = 10000).fit(XX, yy)\n", "scores_cv = -cross_val_score(regr_cv, XX, yy, cv=LeaveOneOut(), scoring='neg_mean_absolute_error')\n", "\n", "print('cross-validataion result : radius 1 + 2')\n", @@ -1051,7 +981,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1065,7 +995,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/dg_prediction.py b/dg_prediction.py new file mode 100644 index 0000000..4106175 --- /dev/null +++ b/dg_prediction.py @@ -0,0 +1,338 @@ +import streamlit as st +import pandas as pd +import numpy as np +from PIL import Image +import webbrowser +import pickle +import joblib + +from CC.chemaxon import * +from CC.compound import Compound +from CC.compound_cacher import CompoundCacher +from rdkit.Chem import rdChemReactions as Reactions +from rdkit.Chem import Draw +from rdkit import Chem + +import json, sys, re, os + +class dGPredictor: + + def __init__(self, smiles_compounds_path=None, mol_sig_r1_path=None, + mol_sig_r2_path=None, model_file_path=None): + # load_smiles + smiles_compounds_path = smiles_compounds_path or os.path.join( + os.path.dirname(__file__), 'data/cache_compounds_20160818.csv') + db = pd.read_csv(smiles_compounds_path, index_col='compound_id') + self.db_smiles = db['smiles_pH7'].to_dict() + + # load_molsig_rad1 + mol_sig_r1_path = mol_sig_r1_path or os.path.join( + os.path.dirname(__file__), 'data/decompose_vector_ac.json') + self.mol_sig_r1 = json.load(open(mol_sig_r1_path)) + + # load_molsig_rad2 + mol_sig_r2_path = mol_sig_r2_path or os.path.join( + os.path.dirname(__file__), 'data/decompose_vector_ac_r2_py3_indent_modified_manual.json') + self.mol_sig_r2 = json.load(open(mol_sig_r2_path)) + + # load_model + model_file_path = model_file_path or os.path.join(os.path.dirname(__file__), 'model/M12_model_BR.pkl') + self.model = joblib.load(open(model_file_path, 'rb')) + + # load_compound_cache + self.ccache = CompoundCacher() + + def predict(self, rxn_str, rxnID, pH, I, extra_info=None, draw=True, printing=True): + # parameterize novel contributions + novel_mets = dGPredictor.parse_novel_molecule(extra_info) + novel_smiles = dGPredictor.parse_novel_smiles(novel_mets) + novel_decomposed_r1 = dGPredictor.decompse_novel_mets_rad1(novel_smiles) + novel_decomposed_r2 = dGPredictor.decompse_novel_mets_rad2(novel_smiles) + + # draw the simulated reaction + rxn_dict = dGPredictor.parse_formula(rxn_str) + if draw: + dGPredictor.draw_rxn_figure(rxn_dict, self.db_smiles, novel_smiles) + + # estimate the dG for the reaction + mu, std, rule_df1, rule_df2 = dGPredictor.get_dG0( + rxn_dict, rxnID, pH, I, self.model, self.molsig_r1, self.molsig_r2, + novel_decomposed_r1, novel_decomposed_r2, novel_mets) + if printing: + print(f"{rxnID}:\tdG = {mu:.2f} ± {std:.2f} kJ/mol") + return mu, std, rule_df1, rule_df2 + + def bulk_prediction(self, RXNs, pH, I, extra_info=None, draw=True, printing=True): + return {rxnID: self.predict(rxn_str, rxnID, pH, I, extra_info, draw, printing) + for rxnID, rxn_str in RXNs.items()} + + @staticmethod + def count_substructures(radius, molecule): + """Helper function for get the information of molecular signature of a + metabolite. The relaxed signature requires the number of each substructure + to construct a matrix for each molecule. + Parameters + ---------- + radius : int + the radius is bond-distance that defines how many neighbor atoms should + be considered in a reaction center. + molecule : Molecule + a molecule object create by RDkit (e.g. Chem.MolFromInchi(inchi_code) + or Chem.MolToSmiles(smiles_code)) + Returns + ------- + dict + dictionary of molecular signature for a molecule, + {smiles: molecular_signature} + """ + m = molecule + smi_count = dict() + atomList = [atom for atom in m.GetAtoms()] + + for i in range(len(atomList)): + env = Chem.FindAtomEnvironmentOfRadiusN(m, radius, i) + atoms = set() + for bidx in env: + atoms.add(m.GetBondWithIdx(bidx).GetBeginAtomIdx()) + atoms.add(m.GetBondWithIdx(bidx).GetEndAtomIdx()) + + # only one atom is in this environment, such as O in H2O + if len(atoms) == 0: + atoms = {i} + + smi = Chem.MolFragmentToSmiles(m, atomsToUse=list(atoms), + bondsToUse=env, canonical=True) + + if smi in smi_count: + smi_count[smi] = smi_count[smi] + 1 + else: + smi_count[smi] = 1 + return smi_count + + @staticmethod + def decompse_novel_mets_rad1(novel_smiles, radius=1): + decompose_vector = dict() + + for cid, smiles_pH7 in novel_smiles.items(): + mol = Chem.MolFromSmiles(smiles_pH7) + mol = Chem.RemoveHs(mol) + # Chem.RemoveStereochemistry(mol) + smi_count = dGPredictor.count_substructures(radius, mol) + decompose_vector[cid] = smi_count + return decompose_vector + + @staticmethod + def decompse_novel_mets_rad2(novel_smiles, radius=2): + decompose_vector = dict() + + for cid, smiles_pH7 in novel_smiles.items(): + mol = Chem.MolFromSmiles(smiles_pH7) + mol = Chem.RemoveHs(mol) + # Chem.RemoveStereochemistry(mol) + smi_count = dGPredictor.count_substructures(radius, mol) + decompose_vector[cid] = smi_count + return decompose_vector + + @staticmethod + def parse_reaction_formula_side(s): + """ + Parses the side formula, e.g. '2 C00001 + C00002 + 3 C00003' + Ignores stoichiometry. + + Returns: + The set of CIDs. + """ + if s.strip() == "null": + return {} + + compound_bag = {} + for member in re.split('\s+\+\s+', s): + tokens = member.split(None, 1) + if len(tokens) == 0: + continue + if len(tokens) == 1: + amount = 1 + key = member + else: + amount = float(tokens[0]) + key = tokens[1] + + compound_bag[key] = compound_bag.get(key, 0) + amount + + return compound_bag + + @staticmethod + def parse_formula(formula, arrow='<=>', rid=None): + """ + Parses a two-sided formula such as: 2 C00001 => C00002 + C00003 + + Return: + The set of substrates, products and the direction of the reaction + """ + tokens = formula.split(arrow) + if len(tokens) < 2: + print(('Reaction does not contain the arrow sign (%s): %s' + % (arrow, formula))) + if len(tokens) > 2: + print(('Reaction contains more than one arrow sign (%s): %s' + % (arrow, formula))) + + left = tokens[0].strip() + right = tokens[1].strip() + + sparse_reaction = {} + for cid, count in dGPredictor.parse_reaction_formula_side(left).items(): + sparse_reaction[cid] = sparse_reaction.get(cid, 0) - count + + for cid, count in dGPredictor.parse_reaction_formula_side(right).items(): + sparse_reaction[cid] = sparse_reaction.get(cid, 0) + count + + return sparse_reaction + + @staticmethod + def draw_rxn_figure(rxn_dict, db_smiles, novel_smiles): + # db_smiles = load_smiles() + + left = '' + right = '' + + for met, stoic in rxn_dict.items(): + if met == "C00080" or met == "C00282": + continue # hydogen is not considered + if stoic > 0: + if met in db_smiles: + right = right + db_smiles[met] + '.' + else: + right = right + novel_smiles[met] + '.' + else: + if met in db_smiles: + left = left + db_smiles[met] + '.' + else: + left = left + novel_smiles[met] + '.' + smarts = left[:-1] + '>>' + right[:-1] + # print smarts + smarts = str(smarts) + rxn = Reactions.ReactionFromSmarts(smarts, useSmiles=True) + return Chem.Draw.ReactionToImage(rxn) # , subImgSize=(400, 400)) + + @staticmethod + def get_rule(rxn_dict, molsig1, molsig2, novel_decomposed1, novel_decomposed2): + if novel_decomposed1 != None: + for cid in novel_decomposed1: + molsig1[cid] = novel_decomposed1[cid] + if novel_decomposed2 != None: + for cid in novel_decomposed2: + molsig2[cid] = novel_decomposed2[cid] + + molsigna_df1 = pd.DataFrame.from_dict(molsig1).fillna(0) + all_mets1 = molsigna_df1.columns.tolist() + all_mets1.append("C00080") + all_mets1.append("C00282") + + molsigna_df2 = pd.DataFrame.from_dict(molsig2).fillna(0) + all_mets2 = molsigna_df2.columns.tolist() + all_mets2.append("C00080") + all_mets2.append("C00282") + + moieties_r1 = open(os.path.join(os.path.dirname(__file__), 'data/group_names_r1.txt')) + moieties_r2 = open(os.path.join(os.path.dirname(__file__), 'data/group_names_r2_py3_modified_manual.txt')) + moie_r1 = moieties_r1.read().splitlines() + moie_r2 = moieties_r2.read().splitlines() + + molsigna_df1 = molsigna_df1.reindex(moie_r1) + molsigna_df2 = molsigna_df2.reindex(moie_r2) + + rule_df1 = pd.DataFrame(index=molsigna_df1.index) + rule_df2 = pd.DataFrame(index=molsigna_df2.index) + # for rid, value in reaction_dict.items(): + # # skip the reactions with missing metabolites + # mets = value.keys() + # flag = False + # for met in mets: + # if met not in all_mets: + # flag = True + # break + # if flag: continue + + rule_df1['change'] = 0 + for met, stoic in rxn_dict.items(): + if met == "C00080" or met == "C00282": + continue # hydogen is zero + rule_df1['change'] += molsigna_df1[met] * stoic + + rule_df2['change'] = 0 + for met, stoic in rxn_dict.items(): + if met == "C00080" or met == "C00282": + continue # hydogen is zero + rule_df2['change'] += molsigna_df2[met] * stoic + + rule_vec1 = rule_df1.to_numpy().T + rule_vec2 = rule_df2.to_numpy().T + + m1, n1 = rule_vec1.shape + m2, n2 = rule_vec2.shape + + zeros1 = np.zeros((m1, 44)) + zeros2 = np.zeros((m2, 44)) + X1 = np.concatenate((rule_vec1, zeros1), 1) + X2 = np.concatenate((rule_vec2, zeros2), 1) + + rule_comb = np.concatenate((X1, X2), 1) + + # rule_df_final = {} + # rule_df_final['rad1'] = rule_df1 + # rule_df_final['rad2'] = rule_df2 + return rule_comb, rule_df1, rule_df2 + + @staticmethod + def get_ddG0(rxn_dict, pH, I, novel_mets): + ccache = CompoundCacher() + # ddG0 = get_transform_ddG0(rxn_dict, ccache, pH, I, T) + T = 298.15 + ddG0_forward = 0 + for compound_id, coeff in rxn_dict.items(): + if novel_mets != None and compound_id in novel_mets: + comp = novel_mets[compound_id] + else: + comp = ccache.get_compound(compound_id) + ddG0_forward += coeff * comp.transform_pH7(pH, I, T) + + return ddG0_forward + + @staticmethod + def get_dG0(rxn_dict, rid, pH, I, loaded_model, molsig_r1, molsig_r2, novel_decomposed_r1, novel_decomposed_r2, + novel_mets): + + # rule_df = get_rxn_rule(rid) + rule_comb, rule_df1, rule_df2 = dGPredictor.get_rule( + rxn_dict, molsig_r1, molsig_r2, novel_decomposed_r1, novel_decomposed_r2) + + X = rule_comb + + ymean, ystd = loaded_model.predict(X, return_std=True) + + result = {} + # result['dG0'] = ymean[0] + get_ddG0(rxn_dict, pH, I) + # result['standard deviation'] = ystd[0] + + # result_df = pd.DataFrame([result]) + # result_df.style.hide_index() + # return result_df + return ymean[0] + dGPredictor.get_ddG0(rxn_dict, pH, I, novel_mets), ystd[0], rule_df1, rule_df2 + # return ymean[0],ystd[0] + + @staticmethod + def parse_novel_molecule(add_info): + result = {} + for cid, InChI in add_info.items(): + c = Compound.from_inchi('Test', cid, InChI) + result[cid] = c + return result + + @staticmethod + def parse_novel_smiles(result): + novel_smiles = {} + for cid, c in result.items(): + smiles = c.smiles_pH7 + novel_smiles[cid] = smiles + return novel_smiles \ No newline at end of file diff --git a/examples/.ipynb_checkpoints/ModelSEED_model-checkpoint.ipynb b/examples/.ipynb_checkpoints/ModelSEED_model-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/examples/.ipynb_checkpoints/ModelSEED_model-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/ModelSEED_model.ipynb b/examples/ModelSEED_model.ipynb new file mode 100644 index 0000000..bf91a8c --- /dev/null +++ b/examples/ModelSEED_model.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3fb8cc7d-03f1-4176-ac1c-f583f106aa91", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cobrakbase 0.3.1\n" + ] + } + ], + "source": [ + "# import kbase\n", + "import os\n", + "local_cobrakbase_path = os.path.join('C:', 'Users', 'Andrew Freiburger','Documents','Argonne','cobrakbase')\n", + "os.environ[\"HOME\"] = local_cobrakbase_path\n", + "import cobrakbase\n", + "with open(\"C:/Users/Andrew Freiburger/Documents/Argonne/kbase_token.txt\") as token_file:\n", + " kbase = cobrakbase.KBaseAPI(token_file.readline())\n", + "AP49 = kbase.get_from_ws(\"Sphingobium_AP49_pacbio_v2.RAST.fbamodel\", 114731)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6dd07401-766a-4756-989d-6265cf1f8d46", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['OBJECT_TYPE', 'SBML_FIELD_ATTRIBUTES', 'SBML_FIELD_GAPFILLINGS', 'SBML_FIELD_GAPGENS', 'SBML_FIELD_GENOME_REFS', 'SBML_FIELD_MODEL_TYPE', 'SBML_FIELD_SOURCE', 'SBML_FIELD_SOURCE_ID', 'SBML_FIELD_TEMPLATE_REFS', '__VERSION__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__enter__', '__eq__', '__exit__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setstate__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_annotation', '_compartments', '_contexts', '_from_json', '_get_computed_attributes', '_get_gapfillings', '_get_gapgens', '_get_genome_ref', '_get_source', '_get_source_id', '_get_template_ref', '_get_type', '_id', '_model_compartments', '_populate_solver', '_repr_html_', '_set_computed_attributes', '_set_gapfillings', '_set_gapgens', '_set_genome_ref', '_set_id_with_model', '_set_source', '_set_source_id', '_set_template_ref', '_set_type', '_solver', '_to_json', '_to_object', '_tolerance', 'add_boundary', 'add_cons_vars', 'add_groups', 'add_metabolites', 'add_reactions', 'annotation', 'attributes', 'boundary', 'compartments', 'computed_attributes', 'constraints', 'contig_coverages', 'copied', 'copy', 'copy_source_inaccessible', 'created', 'creator', 'data', 'data_keys', 'delete_biomasses', 'deleted_reactions', 'demands', 'epoch', 'exchanges', 'exclude_dict', 'from_kbase_json', 'future__init__', 'gapfilledcandidates', 'gapfillings', 'gapgens', 'genes', 'genome_ref', 'get_associated_groups', 'get_data', 'get_exchange_by_metabolite_id', 'get_kbase_args', 'groups', 'id', 'info', 'loops', 'medium', 'merge', 'metabolites', 'model_edits', 'model_type', 'name', 'notes', 'objective', 'objective_direction', 'optimize', 'orig_wsid', 'other_genome_refs', 'path', 'problem', 'provenance', 'quantopts', 'reactions', 'refs', 'remove_cons_vars', 'remove_groups', 'remove_metabolites', 'remove_reactions', 'repair', 'sinks', 'slim_optimize', 'solver', 'source', 'source_id', 'summary', 'template_ref', 'template_refs', 'tolerance', 'type', 'variables']\n" + ] + } + ], + "source": [ + "print(dir(AP49))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a73999d1-d72e-42ef-9ba9-7891590c5fe5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{}\n" + ] + } + ], + "source": [ + "print(AP49.annotation)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0a6c2f0c-d9b9-4e29-923e-6f86f34d1081", + "metadata": {}, + "outputs": [], + "source": [ + "from modelseedpy.biochem import from_local\n", + "\n", + "msdb = from_local(\"C:\\\\Users\\\\Andrew Freiburger\\\\Documents\\\\Argonne\\\\ModelSEED\\\\ModelSEEDDatabase\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f749c98f-1827-4d1a-9eaf-821ac1e15f79", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cpd00067_e0 + cpd00106_e0 --> cpd00067_c0 + cpd00106_c0\n", + "{'sbo': 'SBO:0000176', 'seed.reaction': 'rxn05561'}\n", + "['__add__', '__class__', '__copy__', '__deepcopy__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__isub__', '__le__', '__lt__', '__module__', '__mul__', '__ne__', '__new__', '__radd__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__weakref__', '_annotation', '_associate_gene', '_check_bounds', '_dissociate_gene', '_genes', '_get_rev_and_max_min_flux', '_gpr', '_id', '_lower_bound', '_metabolites', '_model', '_repr_html_', '_set_id_with_model', '_to_json', '_update_awareness', '_upper_bound', 'add_metabolites', 'aliases', 'annotation', 'boundary', 'bounds', 'build_reaction_from_string', 'build_reaction_string', 'check_mass_balance', 'compartment', 'compartments', 'copy', 'coverage', 'dblinks', 'delete', 'edits', 'flux', 'flux_expression', 'forward_variable', 'from_cobra_reaction', 'from_json', 'functional', 'gapfill_data', 'gene_count', 'gene_name_reaction_rule', 'gene_reaction_rule', 'genes', 'get_coefficient', 'get_coefficients', 'get_compartments', 'gpr', 'id', 'imported_gpr', 'knock_out', 'lower_bound', 'metabolites', 'model', 'modelReactionProteins', 'model_reaction_proteins', 'modelcompartment_ref', 'name', 'notes', 'numerical_attributes', 'objective_coefficient', 'probability', 'products', 'protons', 'reactants', 'reaction', 'reaction_ref', 'reduced_cost', 'remove_from_model', 'reverse_id', 'reverse_variable', 'reversibility', 'string_attributes', 'subsystem', 'subtract_metabolites', 'summary', 'update_genes_from_gpr', 'update_variable_bounds', 'upper_bound', 'x', 'y']\n", + "[H+]\n", + "cpd00067_e0 + cpd00106_e0 --> cpd00067_c0 + cpd00106_c0\n", + "cpd00002_c0 + cpd00032_c0 --> cpd00008_c0 + cpd00011_c0 + cpd00061_c0\n", + "{'sbo': 'SBO:0000176', 'seed.reaction': 'rxn00247'}\n", + "['__add__', '__class__', '__copy__', '__deepcopy__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__isub__', '__le__', '__lt__', '__module__', '__mul__', '__ne__', '__new__', '__radd__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__weakref__', '_annotation', '_associate_gene', '_check_bounds', '_dissociate_gene', '_genes', '_get_rev_and_max_min_flux', '_gpr', '_id', '_lower_bound', '_metabolites', '_model', '_repr_html_', '_set_id_with_model', '_to_json', '_update_awareness', '_upper_bound', 'add_metabolites', 'aliases', 'annotation', 'boundary', 'bounds', 'build_reaction_from_string', 'build_reaction_string', 'check_mass_balance', 'compartment', 'compartments', 'copy', 'coverage', 'dblinks', 'delete', 'edits', 'flux', 'flux_expression', 'forward_variable', 'from_cobra_reaction', 'from_json', 'functional', 'gapfill_data', 'gene_count', 'gene_name_reaction_rule', 'gene_reaction_rule', 'genes', 'get_coefficient', 'get_coefficients', 'get_compartments', 'gpr', 'id', 'imported_gpr', 'knock_out', 'lower_bound', 'metabolites', 'model', 'modelReactionProteins', 'model_reaction_proteins', 'modelcompartment_ref', 'name', 'notes', 'numerical_attributes', 'objective_coefficient', 'probability', 'products', 'protons', 'reactants', 'reaction', 'reaction_ref', 'reduced_cost', 'remove_from_model', 'reverse_id', 'reverse_variable', 'reversibility', 'string_attributes', 'subsystem', 'subtract_metabolites', 'summary', 'update_genes_from_gpr', 'update_variable_bounds', 'upper_bound', 'x', 'y']\n", + "Nc1ncnc2c1ncn2C1OC(COP(=O)([O-])OP(=O)(O)OP(=O)([O-])[O-])C(O)C1O\n", + "cpd00002_c0 + cpd00032_c0 --> cpd00008_c0 + cpd00011_c0 + cpd00061_c0\n", + "cpd00067_e0 + cpd00075_e0 <=> cpd00067_c0 + cpd00075_c0\n", + "{'sbo': 'SBO:0000176', 'seed.reaction': 'rxn05625'}\n", + "['__add__', '__class__', '__copy__', '__deepcopy__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__isub__', '__le__', '__lt__', '__module__', '__mul__', '__ne__', '__new__', '__radd__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__weakref__', '_annotation', '_associate_gene', '_check_bounds', '_dissociate_gene', '_genes', '_get_rev_and_max_min_flux', '_gpr', '_id', '_lower_bound', '_metabolites', '_model', '_repr_html_', '_set_id_with_model', '_to_json', '_update_awareness', '_upper_bound', 'add_metabolites', 'aliases', 'annotation', 'boundary', 'bounds', 'build_reaction_from_string', 'build_reaction_string', 'check_mass_balance', 'compartment', 'compartments', 'copy', 'coverage', 'dblinks', 'delete', 'edits', 'flux', 'flux_expression', 'forward_variable', 'from_cobra_reaction', 'from_json', 'functional', 'gapfill_data', 'gene_count', 'gene_name_reaction_rule', 'gene_reaction_rule', 'genes', 'get_coefficient', 'get_coefficients', 'get_compartments', 'gpr', 'id', 'imported_gpr', 'knock_out', 'lower_bound', 'metabolites', 'model', 'modelReactionProteins', 'model_reaction_proteins', 'modelcompartment_ref', 'name', 'notes', 'numerical_attributes', 'objective_coefficient', 'probability', 'products', 'protons', 'reactants', 'reaction', 'reaction_ref', 'reduced_cost', 'remove_from_model', 'reverse_id', 'reverse_variable', 'reversibility', 'string_attributes', 'subsystem', 'subtract_metabolites', 'summary', 'update_genes_from_gpr', 'update_variable_bounds', 'upper_bound', 'x', 'y']\n", + "[H+]\n", + "cpd00067_e0 + cpd00075_e0 <=> cpd00067_c0 + cpd00075_c0\n" + ] + } + ], + "source": [ + "import re\n", + "for count, rxn in enumerate(AP49.reactions):\n", + " print(rxn.reaction)\n", + " print(rxn.annotation)\n", + " print(dir(rxn))\n", + " for met in rxn.metabolites:\n", + " # print(dir(met))\n", + " print(met.smiles)\n", + " # ms_met = msdb.compounds.get_by_id(re.sub(\"(\\_\\w\\d)\", \"\", met.id))\n", + " # print(dir(ms_met))\n", + " # print(ms_met.inchi)\n", + " break\n", + " print(rxn.reaction)\n", + " if count > 1:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22f78f6f-c43d-4cc7-b362-94815bb93483", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/mscompatability.log b/examples/mscompatability.log new file mode 100644 index 0000000..e69de29 diff --git a/model/M12_model_BR.pkl b/model/M12_model_BR.pkl new file mode 100644 index 0000000..3331782 --- /dev/null +++ b/model/M12_model_BR.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e324cef62771f8ed19387c38b3646b8cf397404fa2bac013099ed3c6fdd9b8f4 +size 205129939 diff --git a/retrieve_bulk.ipynb b/retrieve_bulk.ipynb index 0fc2484..1166eb3 100644 --- a/retrieve_bulk.ipynb +++ b/retrieve_bulk.ipynb @@ -9,7 +9,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2021-08-13 17:29:46.477 INFO rdkit: Enabling RDKit 2021.03.4 jupyter extensions\n" + "2023-01-19 17:47:51.673 INFO rdkit: Enabling RDKit 2022.09.3 jupyter extensions\n" ] } ], @@ -390,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -398,6 +398,7 @@ "molsig_r1 = load_molsig_rad1()\n", "molsig_r2 = load_molsig_rad2()\n", "\n", + "# the model_gen.py must first be executed\n", "loaded_model = load_model()\n", "ccache = load_compound_cache()" ] @@ -411,25 +412,25 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "rxn_str = 'C01745 + C00004 <=> N00001 + C00003 + C00001'" + "rxn_str = 'C01745 + C00004 --> N00001 + C00003 + C00001'" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'C01745 + C00004 <=> N00001 + C00003 + C00001'" + "'C01745 + C00004 --> N00001 + C00003 + C00001'" ] }, - "execution_count": 13, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -440,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -449,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -458,7 +459,7 @@ "'InChI=1S/C14H12O/c15-14-8-4-7-13(11-14)10-9-12-5-2-1-3-6-12/h1-11,15H/b10-9+'" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -469,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -479,14 +480,21 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-01-19 17:58:55.637 WARNING root: chemaxon failed to find pKas for this molecule: InChI=1S/C14H12O/c15-14-8-4-7-13(11-14)10-9-12-5-2-1-3-6-12/h1-11,15H/b10-9+\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "{'N00001': 'Oc1cccc(/C=C/c2ccccc2)c1'}\n" + "{'N00001': 'c1ccc(cc1)/C=C/c1cccc(c1)O'}\n" ] } ], @@ -508,7 +516,25 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c1ccc(cc1)/C=C/c1cccc(c1)O\n" + ] + } + ], + "source": [ + "# dir(novel_mets['N00001'])\n", + "print((novel_mets[\"N00001\"].smiles_pH7))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -517,17 +543,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -538,22 +564,21 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "dG = -121.79 ± 100.57 kJ/mol\n" + "dG = -121.74 ± 97.58 kJ/mol\n" ] } ], "source": [ "mu, std, rule_df1, rule_df2 = get_dG0(rxn_dict, 'R00801', pH, I, loaded_model, molsig_r1, molsig_r2, novel_decomposed_r1, novel_decomposed_r2, novel_mets)\n", "\n", - "print(\"dG = %.2f ± %.2f kJ/mol\" % (mu, std))\n", - "\n" + "print(\"dG = %.2f ± %.2f kJ/mol\" % (mu, std))" ] }, { @@ -565,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -580,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -598,10 +623,10 @@ "dG = -18.78 ± 3.37 kJ/mol\n", "R07294\n", "C15524 + C00001 <=> C02137 + C00010\n", - "dG = -14.46 ± 31.43 kJ/mol\n", + "dG = -14.46 ± 31.84 kJ/mol\n", "R01252\n", "C00148 + C00026 + C00007 <=> C01157 + C00042 + C00011\n", - "dG = -427.04 ± 41.12 kJ/mol\n", + "dG = -427.04 ± 50.51 kJ/mol\n", "R00406\n", "C00091 + C00149 <=> C00042 + C04348\n", "dG = -3.27 ± 4.37 kJ/mol\n" @@ -638,7 +663,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -652,7 +677,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.12" } }, "nbformat": 4,