{ "cells": [ { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as p\n", "import matplotlib.pyplot as plt\n", "from sklearn import linear_model\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.neural_network import MLPRegressor" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset = p.read_csv(\"Downloads/mix.csv\", sep = \";\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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NSCsmilespKaH_IDqHqOqOdacid
0NSC_10232Fc1c(cccc1)OCC(=O)O3.08190.489031-0.739889-0.532442True
1NSC_102796c1cc(cc(c1OCCCC(=O)O)C)Cl4.84280.481782-0.737343-0.536813True
2NSC_10312Fc1ccc(c(F)c1)C(=O)O3.58150.487173-0.758570-0.517106True
3NSC_10319Fc1c(cccc1)C(=O)O3.27150.485349-0.757463-0.517331True
4NSC_10320Fc1cc(ccc1)C(=O)O3.86150.486689-0.752247-0.512832True
5NSC_10321Fc1ccc(cc1)C(=O)O4.14150.485683-0.754989-0.516351True
6NSC_106449C(=O)(O)CCC(=O)O4.2190.481352-0.736415-0.535495True
7NSC_1112C1C(C1)C(=O)O4.83120.480072-0.737349-0.530925True
8NSC_11765C(CC)(CC)C(=O)O4.71200.479354-0.742006-0.532430True
9NSC_120417c1ccccc1[C@@H](c1ccccc1)C(=O)O3.94280.481019-0.736494-0.518805True
10NSC_12096c1(c(c(c(cc1C(=O)O)C(=O)O)C(=O)O)C(=O)O)C(=O)O1.80270.494718-0.752332-0.491932True
11NSC_125718c1ccccc1CC(=O)O4.31180.480703-0.735679-0.531820True
12NSC_126584Fc1cccc(F)c1C(=O)O2.85150.486151-0.719989-0.508648True
13NSC_132953C(=O)(O)C4.7650.480616-0.725817-0.543885True
14NSC_13564c1cc(cc(c1C(=O)O)O)O3.11170.482454-0.759200-0.519944True
15NSC_141BrCC(=O)O2.8980.490306-0.729924-0.526733True
16NSC_14190O1[C@@]23[C@@H]4CC[C@]5(C(=C)C[C@]4([C@H]([C@@...4.00420.482679-0.742933-0.524160True
17NSC_142ClCC(=O)O2.8780.490754-0.728108-0.524686True
18NSC_14285Clc1ccc(cc1)CC(=O)O4.19180.482455-0.734872-0.526215True
19NSC_14358Brc1ccc(cc1)CC(=O)O4.19180.483267-0.735217-0.528036True
20NSC_147400c1c(c(cc(c1C(=O)O)C)C)C4.38210.481206-0.754803-0.514740True
21NSC_149c1c(cccc1)C(=O)O4.19150.483660-0.754053-0.516827True
22NSC_15042c1(c(cccc1)C(=O)O)Cl2.89150.485712-0.756333-0.512407True
23NSC_151909c1cccc2c1cc1ccccc1c2C(=O)O3.65270.480664-0.723346-0.504408True
24NSC_15310c1c(cccc1C(=O)O)C(=O)O3.70180.486608-0.756723-0.514222True
25NSC_15772c1cccc2c1c(ccc2)CC(=O)O4.23240.481026-0.737732-0.528844True
26NSC_15797O=Cc1ccc(cc1)C(=O)O3.77160.487109-0.752144-0.509275True
27NSC_16045C(C)C(C)(C)C(=O)O5.03200.479349-0.746686-0.539767True
28NSC_166OCC(=O)O3.8390.486285-0.737352-0.541080True
29NSC_16631c1(c(ccc(c1)C(=O)O)O)O4.26150.484390-0.755481-0.514857True
...........................
341NSC_8130c1(cc(cc(c1O)C)C)C(C)(C)C12.04160.463194-0.776039-0.487273False
342NSC_8204c1cccc(c1C(=O)OC)O9.87190.463725-0.727542-0.425201False
343NSC_82996O=Cc1ccc(c(c1)O)OC8.89190.460785-0.757707-0.469153False
344NSC_8464Clc1cc(ccc1O)C(C)(C)C8.58160.462662-0.753492-0.452375False
345NSC_8475c1cc(ccc1C(=O)OCCCC)O8.47190.463051-0.762809-0.444693False
346NSC_8477c1c(ccc(c1C)O)C(C)(C)C10.59190.456479-0.771014-0.482809False
347NSC_8510c1cc(ccc1C(=O)OCC)O8.34170.463171-0.762557-0.444474False
348NSC_8511c1cc(ccc1C(=O)OCCC)O7.91180.463091-0.762669-0.444615False
349NSC_85228C(C)O15.9090.434784-0.740200-0.656838False
350NSC_85232CO15.3060.434178-0.733705-0.661997False
351NSC_85475c1(cc(c(cc1)O)CO)C10.15190.456658-0.773334-0.485838False
352NSC_87078Fc1cc(ccc1)O9.21130.462378-0.764281-0.475697False
353NSC_8768c1c(cccc1O)C10.09160.457939-0.768640-0.483908False
354NSC_88303c1c(ccc(c1)O)C(F)(F)F8.68160.463668-0.762624-0.461167False
355NSC_8837C(C)OCCO14.80160.435828-0.734773-0.644280False
356NSC_8873c1c(cccc1O)CC9.90190.457912-0.768762-0.483552False
357NSC_8885c1c(cc(cc1O)C)CC10.10220.457785-0.770413-0.483136False
358NSC_8895c1c(c(ccc1CC=C)O)OC10.19190.457462-0.760319-0.468112False
359NSC_91527c1(ccc(cc1)[C@@H](c1ccc(cc1)O)c1ccccc1CO)O9.65390.458003-0.768359-0.471529False
360NSC_9230OCC(O)CO14.4070.437357-0.732922-0.635230False
361NSC_9247c1c(ccc(c1)O)O10.85130.456512-0.768012-0.483003False
362NSC_9268c1c(cc(cc1C)C)O10.19190.457821-0.770308-0.483535False
363NSC_93876OCCO15.1050.436004-0.733223-0.644783False
364NSC_9586c1(c2ccccc2ccc1)O9.34190.461046-0.767400-0.441500False
365NSC_96336C(F)(F)(F)C(O)C(F)(F)F9.30120.471990-0.722442-0.591205False
366NSC_9775C1c2ccc(cc2CC1)O10.32200.456841-0.770016-0.482531False
367NSC_98355c1c(ccc(c1C)O)C(C)(C)C10.59190.456479-0.771014-0.482809False
368NSC_9884c1c(cc(cc1)O)C(F)(F)F8.95160.462942-0.762941-0.472215False
369NSC_9885c1c(ccc(c1)O)OCC10.13150.455104-0.769984-0.490652False
370NSC_9887c1(c(c(c(c(c1)Cl)O)Cc1c(c(cc(c1O)Cl)Cl)Cl)Cl)Cl4.95270.467314-0.747633-0.402338False
\n", "

371 rows × 8 columns

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" ], "text/plain": [ " NSC smiles pKa \\\n", "0 NSC_10232 Fc1c(cccc1)OCC(=O)O 3.08 \n", "1 NSC_102796 c1cc(cc(c1OCCCC(=O)O)C)Cl 4.84 \n", "2 NSC_10312 Fc1ccc(c(F)c1)C(=O)O 3.58 \n", "3 NSC_10319 Fc1c(cccc1)C(=O)O 3.27 \n", "4 NSC_10320 Fc1cc(ccc1)C(=O)O 3.86 \n", "5 NSC_10321 Fc1ccc(cc1)C(=O)O 4.14 \n", "6 NSC_106449 C(=O)(O)CCC(=O)O 4.21 \n", "7 NSC_1112 C1C(C1)C(=O)O 4.83 \n", "8 NSC_11765 C(CC)(CC)C(=O)O 4.71 \n", "9 NSC_120417 c1ccccc1[C@@H](c1ccccc1)C(=O)O 3.94 \n", "10 NSC_12096 c1(c(c(c(cc1C(=O)O)C(=O)O)C(=O)O)C(=O)O)C(=O)O 1.80 \n", "11 NSC_125718 c1ccccc1CC(=O)O 4.31 \n", "12 NSC_126584 Fc1cccc(F)c1C(=O)O 2.85 \n", "13 NSC_132953 C(=O)(O)C 4.76 \n", "14 NSC_13564 c1cc(cc(c1C(=O)O)O)O 3.11 \n", "15 NSC_141 BrCC(=O)O 2.89 \n", "16 NSC_14190 O1[C@@]23[C@@H]4CC[C@]5(C(=C)C[C@]4([C@H]([C@@... 4.00 \n", "17 NSC_142 ClCC(=O)O 2.87 \n", "18 NSC_14285 Clc1ccc(cc1)CC(=O)O 4.19 \n", "19 NSC_14358 Brc1ccc(cc1)CC(=O)O 4.19 \n", "20 NSC_147400 c1c(c(cc(c1C(=O)O)C)C)C 4.38 \n", "21 NSC_149 c1c(cccc1)C(=O)O 4.19 \n", "22 NSC_15042 c1(c(cccc1)C(=O)O)Cl 2.89 \n", "23 NSC_151909 c1cccc2c1cc1ccccc1c2C(=O)O 3.65 \n", "24 NSC_15310 c1c(cccc1C(=O)O)C(=O)O 3.70 \n", "25 NSC_15772 c1cccc2c1c(ccc2)CC(=O)O 4.23 \n", "26 NSC_15797 O=Cc1ccc(cc1)C(=O)O 3.77 \n", "27 NSC_16045 C(C)C(C)(C)C(=O)O 5.03 \n", "28 NSC_166 OCC(=O)O 3.83 \n", "29 NSC_16631 c1(c(ccc(c1)C(=O)O)O)O 4.26 \n", ".. ... ... ... \n", "341 NSC_8130 c1(cc(cc(c1O)C)C)C(C)(C)C 12.04 \n", "342 NSC_8204 c1cccc(c1C(=O)OC)O 9.87 \n", "343 NSC_82996 O=Cc1ccc(c(c1)O)OC 8.89 \n", "344 NSC_8464 Clc1cc(ccc1O)C(C)(C)C 8.58 \n", "345 NSC_8475 c1cc(ccc1C(=O)OCCCC)O 8.47 \n", "346 NSC_8477 c1c(ccc(c1C)O)C(C)(C)C 10.59 \n", "347 NSC_8510 c1cc(ccc1C(=O)OCC)O 8.34 \n", "348 NSC_8511 c1cc(ccc1C(=O)OCCC)O 7.91 \n", "349 NSC_85228 C(C)O 15.90 \n", "350 NSC_85232 CO 15.30 \n", "351 NSC_85475 c1(cc(c(cc1)O)CO)C 10.15 \n", "352 NSC_87078 Fc1cc(ccc1)O 9.21 \n", "353 NSC_8768 c1c(cccc1O)C 10.09 \n", "354 NSC_88303 c1c(ccc(c1)O)C(F)(F)F 8.68 \n", "355 NSC_8837 C(C)OCCO 14.80 \n", "356 NSC_8873 c1c(cccc1O)CC 9.90 \n", "357 NSC_8885 c1c(cc(cc1O)C)CC 10.10 \n", "358 NSC_8895 c1c(c(ccc1CC=C)O)OC 10.19 \n", "359 NSC_91527 c1(ccc(cc1)[C@@H](c1ccc(cc1)O)c1ccccc1CO)O 9.65 \n", "360 NSC_9230 OCC(O)CO 14.40 \n", "361 NSC_9247 c1c(ccc(c1)O)O 10.85 \n", "362 NSC_9268 c1c(cc(cc1C)C)O 10.19 \n", "363 NSC_93876 OCCO 15.10 \n", "364 NSC_9586 c1(c2ccccc2ccc1)O 9.34 \n", "365 NSC_96336 C(F)(F)(F)C(O)C(F)(F)F 9.30 \n", "366 NSC_9775 C1c2ccc(cc2CC1)O 10.32 \n", "367 NSC_98355 c1c(ccc(c1C)O)C(C)(C)C 10.59 \n", "368 NSC_9884 c1c(cc(cc1)O)C(F)(F)F 8.95 \n", "369 NSC_9885 c1c(ccc(c1)O)OCC 10.13 \n", "370 NSC_9887 c1(c(c(c(c(c1)Cl)O)Cc1c(c(cc(c1O)Cl)Cl)Cl)Cl)Cl 4.95 \n", "\n", " H_ID qH qO qOd acid \n", "0 19 0.489031 -0.739889 -0.532442 True \n", "1 28 0.481782 -0.737343 -0.536813 True \n", "2 15 0.487173 -0.758570 -0.517106 True \n", "3 15 0.485349 -0.757463 -0.517331 True \n", "4 15 0.486689 -0.752247 -0.512832 True \n", "5 15 0.485683 -0.754989 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pKaH_IDqHqOqOd
count371.000000371.000000371.000000371.000000371.000000
mean6.50388117.8598380.473013-0.750591-0.509098
std3.6042797.8072580.0148600.0148490.048126
min0.5100005.0000000.429287-0.786837-0.661997
25%3.70000014.0000000.460489-0.762620-0.531205
50%4.80000017.0000000.478824-0.753064-0.513323
75%9.70000020.0000000.484448-0.738481-0.482494
max17.60000082.0000000.500662-0.709112-0.380861
\n", "
" ], "text/plain": [ " pKa H_ID qH qO qOd\n", "count 371.000000 371.000000 371.000000 371.000000 371.000000\n", "mean 6.503881 17.859838 0.473013 -0.750591 -0.509098\n", "std 3.604279 7.807258 0.014860 0.014849 0.048126\n", "min 0.510000 5.000000 0.429287 -0.786837 -0.661997\n", "25% 3.700000 14.000000 0.460489 -0.762620 -0.531205\n", "50% 4.800000 17.000000 0.478824 -0.753064 -0.513323\n", "75% 9.700000 20.000000 0.484448 -0.738481 -0.482494\n", "max 17.600000 82.000000 0.500662 -0.709112 -0.380861" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.describe()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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LEtrv4mpcZh5V3amq01z1F14H/l0hRD/JrU3nM3Gc91d18oRxgWbzsReGdCUZ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\nRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "acids = dataset[dataset.acid==True]\n", "alcohols = dataset[dataset.acid==False]\n", "plt.subplot(1,3,1)\n", "plt.scatter(acids.pKa, acids.qH)\n", "plt.scatter(alcohols.pKa, alcohols.qH, c=\"red\")\n", "plt.subplot(1,3,2)\n", "plt.scatter(acids.pKa, acids.qO)\n", "plt.scatter(alcohols.pKa, alcohols.qO, c=\"r\")\n", "plt.subplot(1,3,3)\n", "plt.scatter(acids.pKa, acids.qOd)\n", "plt.scatter(alcohols.pKa, alcohols.qOd, color=\"red\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regr = linear_model.LinearRegression()\n", "regr.fit(dataset.iloc[:,4:9],dataset[[\"pKa\"]])" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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EpCb+H4lGVOsSjQjJAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(dataset[\"pKa\"], regr.predict(dataset.iloc[:,4:9]))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9708817971616116" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regr.score(dataset.iloc[:,4:9],dataset[[\"pKa\"]])" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", " max_features='auto', max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf = RandomForestRegressor()\n", "rf.fit(dataset.iloc[:,4:9],dataset[\"pKa\"])" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "image/png": 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MgT/R4m/1YGZZ8kH/Lne/t3S/u7/u7m8Gj7cAWTM7sc7dLO1TLvh9CLiP/J/C\nxZrqHAcuBp5w95dLdzTjOQ68XBgiC34fCmnTVOfazK4BLgGuDr6spijj81M37v6yu0+4+1Hg2xF9\nabZzPBtYBdwd1aaW57gZA//jwOlmdlpwhXcVsLmkzWbgk0HmybnAa0V/TtdVME73HeApd/9qRJt3\nBe0wsyXkz/uv6tfLKf2ZZ2bHFR6Tv6G3p6RZ05zjIpFXSM12jotsBj4VPP4U8MOQNuV85uvC8sup\n/jVwmbsfiWhTzuenbkruPf1xRF+a5hwHPgw87e4HwnbW/BzX+q52lXfCV5DPjnkW+Fyw7Vrg2uCx\nkV/28VlgN9DXwL6eR/7P9yeBncHPipL+Xg/sJZ9JsB34QIPP77uDvuwK+tXU5zjozzzygfydRdua\n6hyT/1I6CIyRH0P+DPD7wL8CzwAPAicEbecDW4peO+Uz36D+7ic/Fl74LN9a2t+oz08D+/x/g8/o\nk+SD+cnNfI6D7bcXPrtFbet2jjVzV0SkzTTjUI+IiNSQAr+ISJtR4BcRaTMK/CIibUaBX0SkzSjw\ni4i0GQV+EZE2o8AvItJm/hPjZ5jezcD/ygAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(dataset[\"pKa\"], rf.predict(dataset.iloc[:,4:9]))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.99346484000632207" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf.score(dataset.iloc[:,4:9],dataset[[\"pKa\"]])" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.01, max_iter=10000, momentum=0.9,\n", " nesterovs_momentum=True, power_t=0.5, random_state=None,\n", " shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n", " verbose=False, warm_start=False)" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nn = MLPRegressor(max_iter=10000,learning_rate_init=0.01)\n", "nn.fit(dataset.iloc[:,4:9],dataset[\"pKa\"])" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(dataset[\"pKa\"], nn.predict(dataset.iloc[:,4:9]))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.94668430220877675" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nn.score(dataset.iloc[:,4:9],dataset[[\"pKa\"]])" ] } ], "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }