Auto3D to make optimize tautomers and 3D conformers

Playthrough on using Auto3D.
Published

January 16, 2023

import os
import sys
import copy 
root = os.path.dirname(os.path.dirname(os.path.abspath("__file__")))
sys.path.append(root)

View the test molecules

import pandas as pd
import numpy as np 
from rdkit.Chem import PandasTools
import rdkit
from rdkit import Chem #This gives us most of RDkits's functionality
from rdkit.Chem import Draw
from rdkit.Chem.Draw import IPythonConsole #Needed to show molecules
IPythonConsole.ipython_useSVG=True  #SVG's tend to look nicer than the png counterparts
print(rdkit.__version__)

# Mute all errors except critical
Chem.WrapLogs()
lg = rdkit.RDLogger.logger() 
lg.setLevel(rdkit.RDLogger.CRITICAL)
2021.09.4
test_smi = pd.read_csv('./tauto.smi', sep=' ', header=None, names=['SMILES','Name'])
test_smi.sample(2)
SMILES Name
1 C1(=CC(=NC(=C1)Cl)Cl)O Cl-pyridone
4 N=C(C1=C(OCC2=C(F)C=CC=C2)C=CC=C1)O boo
PandasTools.AddMoleculeColumnToFrame(test_smi, smilesCol='SMILES')
PandasTools.FrameToGridImage(test_smi, legendsCol='Name', molsPerRow=4)

Generate low-energy 3D structures with Auto3D

#Always ensure that you have the latest version
import Auto3D
from Auto3D.auto3D import options, main
print(Auto3D.__version__)
2.0
options
<function Auto3D.auto3D.options(path, k=False, window=False, verbose=False, job_name='', enumerate_tautomer=False, tauto_engine='rdkit', pKaNorm=True, isomer_engine='rdkit', enumerate_isomer=True, mode_oe='classic', mpi_np=4, max_confs=None, use_gpu=True, gpu_idx=0, capacity=42, optimizing_engine='AIMNET', patience=1000, opt_steps=5000, convergence_threshold=0.003, threshold=0.3, memory=None, batchsize_atoms=1024)>

import Auto3D from Auto3D.auto3D import options, main

path = ‘./tauto.smi’ args = options(path, window=5, #Outputs the structures whose energies are within a window (kcal/mol) enumerate_tautomer = True, #For enumerating tautomers enumerate_isomer = True, isomer_engine=‘rdkit’, optimizing_engine=‘ANI2xt’, use_gpu=False) #args specify the parameters for Auto3D out = main(args) #main acceps the parameters and run Auto3D print(out)

out = '/lrlhps/users/l017301/SHARE/PROJECTS/SOFTWARE/Auto3D/Jan14/20230116-172252-638797_tauto/tauto_out.sdf'
out_folder = os.path.dirname(out)

Read the SDF files

import mols2grid
from rdkit import Chem
from rdkit.Chem import AllChem
sdf_df = PandasTools.LoadSDF(out)
sdf_df
ID E_tot fmax Converged E_rel(kcal/mol) ROMol
0 Cl-pyridone@taut1 -1242.4485217285633 0.0028711010236293077 True 0.0 <img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/>
1 Cl-pyridone@taut2 -1242.4576208991289 0.0029037443455308676 True 0.0 <img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/>
2 benzene-fused@taut1 -646.5230541429044 0.0028541951905936003 True 0.0 <img data-content="rdkit/molecule" 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" alt="Mol"/>
3 benzene-fused@taut2 -646.5449096909285 0.00297327502630651 True 0.0 <img data-content="rdkit/molecule" 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" alt="Mol"/>
4 benzene-fused@taut3 -646.5199764570714 0.0022692130878567696 True 0.0 <img data-content="rdkit/molecule" 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" alt="Mol"/>
... ... ... ... ... ... ...
228 sildenafil@taut9 -1883.1668644994259 0.0027246379759162664 True 2.6457204767372136 <img data-content="rdkit/molecule" 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" alt="Mol"/>
229 sildenafil@taut9 -1883.166294679022 0.0028603156097233295 True 3.0032881786646404 <img data-content="rdkit/molecule" 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" alt="Mol"/>
230 sildenafil@taut9 -1883.1660918444156 0.002793555613607168 True 3.130568815727958 <img data-content="rdkit/molecule" 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" alt="Mol"/>
231 sildenafil@taut9 -1883.1657381802559 0.002911796560510993 True 3.3524964267405757 <img data-content="rdkit/molecule" 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zJgvdgsCQKNHZvk5eU1adKk8PDwzz77bOfOnampqbm5uQ1GT4jqgkVECxa0SbD8/f0BfPrpp4IgmJubN71T+Ne//iUOK/j6+paUlDxptqqqqkGDBgF4/fXXm9O/zs/PT0hIWLBggbe3t7H49boH6u9DfQelacbuZ8+myEiaM4cOHqwLVu/e9Pbb9Pbb5O2tNjUNB6AZR969e7emlVq3bl0TfalGffXVVwAmT54sRvPnn39u0eKtM2IEKZXk7U3bttGqVTXW1n8AYGBgMnXqnrg4SkmhrCyScofciBYEq6ioyMDAwMjI6MMPPwQwTWwBniA2NlYulwOYOnVq03uQuXPnipkoLS1tfjGi6urqtLS0zz//fMqUKfb29j3q3ZLF33nvWIuU90y/Hml5JiSEIiOpuJi8vWn6dDpwgN58k8rLqbycFi68Lu4KNeusqakZPXp0TExMM3d8DeTn52/evDkuLg6AjY1NTU1NK1bSUuIA8r591L9/be/eXwGwtraW7qChOVrWYl26dGn79u29e/cG8MMPPzQ6j1qtjoiIED/dx4cVGkhIUPv6vqNQKE6dOtWiSh4n9mnWjhxJkZG0fDm9+y4tX04//kh//OO/w/ZGRhIRbdlCFhYPd4VE9PrrB4A5s2fPfsatN7BmzRoAEyZM0O5qn0Tcc+Xl5Vla/htY4ODgcPHixbbZ9JO0+KgwLS0NgLm5+cKFC8PDw0NCQqZMmTJu3Dh/f39PT08XF5euXbsKgiAIwlO7Fzk5ZG1NAG3enNXa+h/Kzs6eOnmyysGBBIESE0nTa1apLji9Lh4rqdX03nt05gxpSvPwWAS82uhwf+tkZWUFBwfL5XI/P79t27Zpa7VNOHKEunenlSv/17NnT8DIycmt6dPDbaPFwfr111/FnnsTunTp8vXXXze9HqWSXFwIeNh4aEdyMn30EZ08SaGhDyf6+FB5+ePzVlVVmZqaCoLQnEGTpyosLJw/f754olAQhIiIiGdfZ3NMmEDACVNTWwA+Pj5NdGfbUouvx7K3t1+xYsWBAwesra2NjY1NTU2trKyMjY3NzMwsLS3T09NDQ0NtbGzGjBnT9Hr+/GdcuIB+/bR9t2l/f/j7Iy8PN27UTSFCeXmjX0X/73//q1QqBwwYoDl70zr5+fnLli3bunXr/fv3DQwMgoODP/74475tcu3YtWvXTp70NTAoUiqrJ0+evH37diMjozbY7tNpN6fvvfcegA8++MDc3Lx79+7ljbUTRLRtGwFkakoXLmh3+/X88Y+0YQNdvEgLF9Lq1Y3OsnTpUgDh4eEZGRl5eXmt2EhBQUFYWJipqSkAQRCCgoLauHMzefJk8XOcPn3646MGOqTlYLm7vwpAvFrBy8ur0XkuXSJzcwIoNla7G39UbS0lJNBnn9GRI0+axdfXF8C+ffuGDBkCQKFQODs7BwUFNXrOp4GSkpKIiAgzMzNNpM6fPy/NO2nKuXPnHBwcwsLC2n7TTdNmsM6dI5mMfH1LPvkktWfPMcuWLWt0tpgYEgSaPFmLW26Ne/fuiWelysrKXnnllcev1wPQsWPHUaNG1R/KJ6LS0tKIiAjzB/vWwMDAZ7mS4tm1blhEatq85n3vXqjV6NvXZssWn4KCH8ePr9W8FBMDNzd4e6OyEjU1OHAAXl5a3HKL7d+/f+nSpZaWlsXFxT169OjVq9e4ceMcHR1NTEzUanVZWVlmZmZmZmZubu7PP/9s8+CpEHfv3l27du2XX35ZUlICwM/P7y9/+cvQoUN1+U4AfelUNaDFkA4aRACtWUMA9ez5yEvjxtGwYVRdTWVlFBCgxW222LFjx/z8/MT3bmNj0+hNbzp06ODj4zNnzpzly5d/+eWXubm54hVamhPVI0eO1OLloM8lrQXrzh2ysiITE/rlFxJHuusbN47Wr6eVK3UZrMTERE3r0rlz5+joaPHYoqCg4PDhw+vWrQsNDR0+fHijUXvhhRfEH1xcXBISErR7OehzSQvBOnyYVq0iIqqqotmzSezvVlZScjJFRJC3N3XsSGPHUkkJvfwypafrIlhHj+bMnCkmw8rKasmSJU0P9pSVlZ0+fTouLi4iIiIwMNDR0TEoKMjFxWXXrl3NuTSZkVaCtWkT2dvT8eNERL6+9PnnNGYMWVg8/P6WeEF+aSmdO0ejR7dhsNRqio+ngQPFIua89FJkZGTrxg/1s4Osz7TTeV+4EIsW4dAhADh4EAcOAICzM3x84O8Pb2+88w4AuLrCze2Rm5ZLKCUFy5bhp58AwNoa8+ZtCg+HtfXTFmucnnaQ9Zh2gtWhA6ZPR3Q0AISGYuJEjBgBJ6e6V3ftgkqFW7dgbY0lS9DYzRme2ZYtSEiAlRUsLPDpp5g4EceOAYCNDebNQ3g4GhtNYNLRwq0i//53mJhg2jSMGYMbN5CRgfrXmldUwMkJv/2GXbvw5Ktkn825c/j4Y+zbB7kc69fj9m1cuIATJxARgdmz6x78x9qW1saxZDKsXQt394bTN2/+xsHBolu314OCJLur09GjmDwZcjkABAdj/Hhs2wYrK1haSrVF9jRaCNabb9Y98WXAAGRnP9JcFRUVRUb++c6dOz/9dFImk+zJHGo1NNcry2RQq/GkO1WwtqKF+2NZWDy8dKDBByreUDQgIMDXV8rnvfj4YP9+iPv0PXswfPjTFmCSk/x23Ldu3VIqlT2kfo7j2rVISYGpKRQKxMSAn0iga5LfH+sZL3VqrmHDcOUK/P3x4DISplvt46bhT3fyJGJj64YYmB54XoIl3qK0Z09d18HqPC/ByskBgEe/m8906PkK1oOvLzOdex6CRUR5VVUqhYJ3hfrjeQhWQUGBw7VrDjY2fPZGfzwPwRKfOdCLO1j65HkIVk5ODgBH7mDpk+chWNeuXQMHS8+0+2Cp1eqTJ08C6Mk9d33SjoN1/fr1Dz/80N7e/tChQ8HBwY8/4YLpkO6fpdMKqampX3311e7du6urqwH0799/2rRpffr00XVd7KH29LDxoqKiLVu2bN68OSsrC4CZmdkf/vCHkJAQ8QlVTK+0jxbr8OHDmzZtSkxM1DRRYWFhb7zxRqPfi2f6QK9brNLS0k2bNm3duvXy5csA5HL5xIkTw8LCNLdcZ3pLT1us9PT0jRs37ty5U3y0n4ODw7vvvjtjxgzNY7SYvtPllxofU1FRERsbq+kzCYLg7++fmJjYNreIZVqkR7tCInJxccnIyABgZmY2derUuXPn1n/eJGtH9GhXKAjCa6+9plAo5s2bN2XKlAb3cGftix61WAAqKyuNjY01D4hj7Zd+BYs9N9rxKR2mzzhYTBIcLCYJDhaTBAeLSYKDxSTBwWKS4GAxSXCwmCQ4WEwSHCwmCQ4WkwQHi0mCg8UkwcFikuBgMUlwsJgkOFhMEhwsJgkOFpMEB4tJgoPFJMHBYpLgYDFJcLCYJDhYTBIcLCYJDhaTBAeLSYKDxSTBwWKS4GAxSXCwmCQ4WEwSHCwmCQ4WkwQHi0mCg8UkwcFikuBgMUlwsJgkOFhMEhwsJgkOFpMEB4tJgoPFJMHBYpLgYDFJcLCYJDhYTBIcLCYJDhaTBAeLSYKDxSTBwWKS4GAxSXCwmCQ4WEwSHCwmCQ4Wk8T/AxgxtH4iOBnmAAAC8HpUWHRyZGtpdFBLTCByZGtpdCAyMDIxLjA5LjQAAHicZZBrSFRBFMfnzr37yNXVVnd97K5eV/ORpYjmh8A7gxXah8IUNSxwCdRNIqNISMs0TcxMsSwpxEQJhXSNLMNydwYfQVAphlGKoaGJpiVSfVDLrq2rVgcOvzNnzvmfOfPV2voBiOYENsxP9ADRCxgpMIpkWCkwiWRZZlMQtBpwMhtZO9c7eH+RkLPXyW2EMpsAXBeQYjvtymsJF8CLlNobNwRW02tzILTzrzTz3/Um9X/G2HX/7mA2FlpbhPlzBoydCsBwHAO3QxaK3aw/x3JBkJPwEimQyIBMDuRbgqDcgXdQ8ApHXuETBB2dTNBJaVQ6A6WnCTq7yKHzVtFVQOUKXN2AmxqoNUDtDtw9gIcKeHoZvbQmqNXxWkejTg/03sCHBzwLeFHdl4O8gYNunITlJDJeKndQ+PAyqZPS00vrKHV1U7t7qNzNjPhMYHPgt5jfSysT/VFGRihuz2um5Z1aVJXfgyYHHlBvzYyl8fqwMEtraLi8xXLpYyyZfnKXfjKnkz2t46Q5pZia34WRZGU4bdFV0Gs1dSTkRyFNVh6iC4NzQt8vA633miJjO12EgrgICqrMRJ86YfHW5NKisGJS28sLn88MkbHaTpIQCNC56efW3phXVnNHgzBz04DaqpPQsPYEMk7OCxeXZtCKbzRqZB4hU3sqbqvuQ5EN1UgenYf3Hl9B2fN6/DY5HsvSeHy/Pyk6pr8cd92KF7JVEai75DLOHGqyHm7eRkYf1+E3B7qF4LlRgna9xKXfRgSU0k7o7AQ+eKwUlbzQ0forg7gq/yoampqxnC57KtY8E4rCzqNTrwfQBXOUxW/EF++LHRRyo0JIz/cdOGdMR/aHrljFP0Fq/TTJ6iongd1fUKJkiaTJHGhCYCQ+C5Pp+7RQeud2MDb8DKBx41noxu5Cy+JcJjVUpKPKxHKyoD1CmzrvIR9VPD36MIcun1xGHaVlRPMbnPbzNlUwfhsAAAOIelRYdE1PTCByZGtpdCAyMDIxLjA5LjQAAHicfVZNbyRFDL3nV/Qf2FL5o1xVByQ2ybIgtIkEgftKgBRpxYXw/3m2e7q7cmAmPerxvHI/289P+ef12x9//v31r9dv3799/fdt3m3++uXx59e37XjJ4x3i9X/+5pzb71Jrvfuy+c12/+nzT0/bw8vH+1vk4fm3p5dfN2mbDJzBe8V+fHn+covQ9rB9sEJaWwWwmNLEDZfOCFyOsgNbYWuUwDrAYqPSuMsVKAls3Dzjh1qINABF2eYVqY7U0pUlkXVozbs+6rhC2/bk0DmJEzBmjzsq3YSuUEsoSesJtRGP/YCazNoV2pOAKtXMxTLjTks1WqoaDhWUnxmKmrZM2tSC6vbww+fv+Iafjqcyaks8GjwSP+qamer27FAxGbeGUeSWMmu3BUteHJUq3DOtFxnUzd5BORmAZg4MKS3zyxhLGyhGhppqjfkUoT5yyNKW3pKPDM2XfaKTeR9tr2vKBiDKl5aJOp4ZatFqa/W2QalFKcYJNalY3JDFqE9gR5vQEW75+5xNQ6hqfZEKjQBCfhK/t7pzqI3XjBPNVK86a+Aq2VRD7kX6mC2AoLgrCt3fR2XalwYxBXS2mamkRyqXqchCkxlPN8yCdkHxtERqpTWnj6dj1C5OVF6t7dJTnWtOH4+V1meuZh3zkJT2BenzaWXw1ORpWUeZY504WwwSO3bbUVXLvvJcDaKHNrBLt72zmDnWrvLaz5F6w4R3eqhDg/H7ZeaZyzGY9nWvORyErK2+U1Pw06J9aA8160FgkC7GI5TbDBnfnIH3qny9F6iPCQCrk7NB1Hdfq++4imRWCxfxNZnx/CCdql89QnT70Q/A/9oglIRpdDTHfaahCLvkbgkdcBsxl39nkh65B/PsJ/TT0+Pi8On5989Pj6fn+/ugwfGW078Jl54mzbjaacSEy06zJVz9NFT/Ok7TJFzz9ETGRVfnIw/Qxd4oPg5ucDGKj4MezIo9IidGQRBhOjjDfdTDdNAmJ4owHczhJw6AwC/GwQE8yqG5R46KOLqHyFEUB2cAr5tNDuSjLvaeAshnz9Uj6PtRF7c9clTBzhlAPqpg768Dz6cPH5Dhl8vGqAP54BwTB5CPU9C/euTsqgTnCexFzbQF9iJXcqCcp5yzXMTi0vTA+eweEsBRV/55cCQOCZa4a/eqVP9++88G93f/AUP5uqxCwqcVAAAB6XpUWHRTTUlMRVMgcmRraXQgMjAyMS4wOS40AAB4nC1SO45iQQy8yoYgQY//busJaaVOJmKCDRER15jDb7kheLJczy6Xy/34fj7W3yfL/bZOL329Tv9Ot59zf3db635a57Xs/Hq99Get8331H3Sg5fT4fp67AK3ytW7ra/35PV11TLewC49KYbtcedAUPfAjROtCY1bkhisAywgqAVycfrnSoFA+8NucGLATRcM6px+IRtQkyjkRQp0PUKppU4h0sCQ/GKB3SaITagy8hw7jFGRuGggcLABLvLMqt4sMi5wAxV2ROW0WcpHDenpPENLs6egACMrcEgWaEMOSAZdXF2mW97qpOo/AHuytUSoaNWI+crDDGlgSjlpIsOpaz9JGZ21eZsvDx5Sy5o1sf2rCAYYRrNs9s2jVUrDFmLawCFggI0m2hVh682FI3yk/hk9hed8FgpGGa8MVzK2JHWeTMdmqjwnHq+GUPTC8awkWQBP7PnBExiYm75XZaHbHJN7EGvpZC46AsajLYXzEdihse4HXNBs26XvywPapXS+OzuMK/9Xfa854S4pWg2MZt4x+dcgoeizOUr1mP8Pc677Xt5EYsB2EKQg5CWMdTrp/zN9vS6IalvB2P2j2hVwS3NErtvQwrvZb/Pz7HwUPryNjDpaVAAAAAElFTkSuQmCC" alt="Mol"/>
232 sildenafil@taut9 -1883.1640510409832 0.0027946573682129383 True 4.411192304313834 <img data-content="rdkit/molecule" 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" alt="Mol"/>

233 rows × 6 columns

sdf_df = sdf_df.sort_values('E_tot', ascending=False)
sdf_df = sdf_df.drop_duplicates(subset=['ID'])
sdf_df['parent_id'] = [x.split('@')[0] for x in sdf_df['ID']]
sdf_df['tauto_id'] = [x.split('@')[1] for x in sdf_df['ID']]
sdf_df['SMILES'] = [Chem.MolToSmiles(x) for x in sdf_df['ROMol']]
sdf_df['E_tot'] = sdf_df['E_tot'].astype(float)
sdf_df.columns
Index(['ID', 'E_tot', 'fmax', 'Converged', 'E_rel(kcal/mol)', 'ROMol',
       'parent_id', 'tauto_id', 'SMILES'],
      dtype='object')
sdf_format = sdf_df[['parent_id','tauto_id', 'ID', 'SMILES', 'E_tot', 'fmax', 'Converged', 'E_rel(kcal/mol)']]
# convert Hatrees to kcal/mol http://wild.life.nctu.edu.tw/class/common/energy-unit-conv-table.html
E_rel = []
for parent_id in set(list(sdf_format['parent_id'])):
  sdf_format_per_parent = sdf_format.loc[ sdf_format['parent_id'] == parent_id ]
  sdf_format_per_parent.loc[sdf_format_per_parent.index, 'E_rel_tauto(kcal/mol)'] = (sdf_format_per_parent['E_tot'] - min(sdf_format_per_parent['E_tot'])) * 627.5095
  E_rel.append(sdf_format_per_parent)
sdf_format = pd.concat(E_rel)
/node/scratch/106404095.1.all.normal.q/ipykernel_28232/475714180.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sdf_format_per_parent.loc[sdf_format_per_parent.index, 'E_rel_tauto(kcal/mol)'] = (sdf_format_per_parent['E_tot'] - min(sdf_format_per_parent['E_tot'])) * 627.5095
sdf_format
parent_id tauto_id ID SMILES E_tot fmax Converged E_rel(kcal/mol) E_rel_tauto(kcal/mol)
167 sildenafil taut26 sildenafil@taut26 CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)... -1883.237851 0.0027714346069842577 True 0.0 0.000000
174 sildenafil taut27 sildenafil@taut27 CCCc1nn(C)c2c(=O)nc(-c3cc(S(=O)(=O)N4CCN(C)CC4... -1883.217860 0.0029108182061463594 True 0.0 12.544642
183 sildenafil taut28 sildenafil@taut28 CCCc1nn(C)c2c(O)nc(-c3cc(S(=O)(=O)N4CCN(C)CC4)... -1883.211783 0.002911168849095702 True 0.0 16.358181
155 sildenafil taut24 sildenafil@taut24 CC/C=C1/NN(C)c2c1nc(-c1cc(S(=O)(=O)N3CCN(C)CC3... -1883.198682 0.002975363051518798 True 0.0 24.578853
119 sildenafil taut20 sildenafil@taut20 C/C=C/c1nn(C)c2c1N[C@@H](c1cc(S(=O)(=O)N3CCN(C... -1883.195765 0.002867447677999735 True 0.0 26.409780
210 sildenafil taut5 sildenafil@taut5 C/C=C/C1=C2NC(c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OC... -1883.185570 0.0029683925677090883 True 0.0 32.806838
91 sildenafil taut17 sildenafil@taut17 C/C=C/c1nn(C)c2c1N=C(c1cc(S(=O)(=O)N3CCN(C)CC3... -1883.185264 0.002987172920256853 True 0.0 32.998937
151 sildenafil taut23 sildenafil@taut23 CC/C=C1/NN(C)c2c1[nH]c(-c1cc(S(=O)(=O)N3CCN(C)... -1883.183689 0.0029540995601564646 True 0.0 33.987148
96 sildenafil taut18 sildenafil@taut18 C/C=C/c1nn(C)c2c1NC(c1cc(S(=O)(=O)N3CCN(C)CC3)... -1883.178366 0.0029339187312871218 True 0.0 37.327788
81 sildenafil taut15 sildenafil@taut15 C/C=C/[C@@H]1NN(C)c2c1nc(-c1cc(S(=O)(=O)N3CCN(... -1883.176364 0.0029547689482569695 True 0.0 38.583651
143 sildenafil taut22 sildenafil@taut22 CC/C=C1/NN(C)c2c(O)nc(-c3cc(S(=O)(=O)N4CCN(C)C... -1883.173366 0.0019200812093913555 True 0.0 40.464996
101 sildenafil taut19 sildenafil@taut19 C/C=C/c1nn(C)c2c1N[C@@H](c1cc(S(=O)(=O)N3CCN(C... -1883.172031 0.00290022068656981 True 0.0 41.302550
227 sildenafil taut9 sildenafil@taut9 C/C=C/C1=NN(C)[C@@H]2C(=O)NC(c3cc(S(=O)(=O)N4C... -1883.171081 0.0029343736823648214 True 0.0 41.899119
160 sildenafil taut25 sildenafil@taut25 CCCC1=NN(C)[C@@H]2C(=O)N=C(c3cc(S(=O)(=O)N4CCN... -1883.169082 0.0029732126276940107 True 0.0 43.153355
187 sildenafil taut3 sildenafil@taut3 C/C=C/C1=C2N=C(c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3O... -1883.166379 0.002880740910768509 True 0.0 44.849577
111 sildenafil taut2 sildenafil@taut2 C/C=C/C1=C2N=C(c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3O... -1883.164880 0.0029965881258249283 True 0.0 45.789950
222 sildenafil taut8 sildenafil@taut8 C/C=C/C1=NN(C)[C@@H]2C(=O)N=C(c3cc(S(=O)(=O)N4... -1883.164851 0.0029544399585574865 True 0.0 45.808576
213 sildenafil taut6 sildenafil@taut6 C/C=C\C1=NN(C)C2=C(O)N=C(c3cc(S(=O)(=O)N4CCN(C... -1883.164151 0.0029395974706858397 True 0.0 46.247307
88 sildenafil taut16 sildenafil@taut16 C/C=C/c1nn(C)c2c1=N[C@@H](c1cc(S(=O)(=O)N3CCN(... -1883.159181 0.0029624311719089746 True 0.0 49.366394
72 sildenafil taut14 sildenafil@taut14 C/C=C\[C@@H]1NN(C)c2c1[nH]c(-c1cc(S(=O)(=O)N3C... -1883.159030 0.002975861541926861 True 0.0 49.460966
55 sildenafil taut13 sildenafil@taut13 C/C=C\[C@@H]1NN(C)c2c(O)nc(-c3cc(S(=O)(=O)N4CC... -1883.148282 0.002590285148471594 True 0.0 56.205456
193 sildenafil taut4 sildenafil@taut4 C/C=C/C1=C2NC(c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OC... -1883.144197 0.002940340666100383 True 0.0 58.768723
217 sildenafil taut7 sildenafil@taut7 C/C=C/C1=NN(C)C2=C(O)NC(c3cc(S(=O)(=O)N4CCN(C)... -1883.144103 0.0029915182385593653 True 0.0 58.827650
21 sildenafil taut10 sildenafil@taut10 C/C=C/C1=NN(C)[C@@H]2C(O)=NC(c3cc(S(=O)(=O)N4C... -1883.142340 0.0029348309617489576 True 0.0 59.933997
17 sildenafil taut1 sildenafil@taut1 C/C=C/C1=C2N=C(c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3O... -1883.142246 0.0026598710101097822 True 0.0 59.993093
29 sildenafil taut11 sildenafil@taut11 C/C=C/C1=NN(C)[C@@H]2C1=NC(c1cc(S(=O)(=O)N3CCN... -1883.141071 0.002974058734253049 True 0.0 60.730651
129 sildenafil taut21 sildenafil@taut21 CC/C=C1/NN(C)[C@@H]2C(=O)N=C(c3cc(S(=O)(=O)N4C... -1883.140111 0.0025039315223693848 True 0.0 61.333093
37 sildenafil taut12 sildenafil@taut12 C/C=C\[C@@H]1NN(C)[C@H]2C(=O)N=C(c3cc(S(=O)(=O... -1883.124047 0.002953974064439535 True 0.0 71.413043
9 boo taut2 boo@taut2 NC(=O)c1ccccc1OCc1ccccc1F -845.393300 0.002969717374071479 True 0.0 0.000000
5 boo taut1 boo@taut1 N=C(O)c1ccccc1OCc1ccccc1F -845.367714 0.002988976426422596 True 0.0 16.055548
15 pyridone taut1 pyridone@taut1 O=c1cc[nH]cc1 -323.358696 0.002931158524006605 True 0.0 0.000000
16 pyridone taut2 pyridone@taut2 Oc1ccncc1 -323.357830 0.0027759429067373276 True 0.0 0.543535
3 benzene-fused taut2 benzene-fused@taut2 O=c1ccnc2ccc3ccc[nH]c3c12 -646.544910 0.00297327502630651 True 0.0 0.000000
2 benzene-fused taut1 benzene-fused@taut1 O=c1cc[nH]c2ccc3cccnc3c12 -646.523054 0.0028541951905936003 True 0.0 13.714564
4 benzene-fused taut3 benzene-fused@taut3 Oc1ccnc2ccc3cccnc3c12 -646.519976 0.0022692130878567696 True 0.0 15.645841
1 Cl-pyridone taut2 Cl-pyridone@taut2 Oc1cc(Cl)nc(Cl)c1 -1242.457621 0.0029037443455308676 True 0.0 0.000000
0 Cl-pyridone taut1 Cl-pyridone@taut1 O=c1cc(Cl)[nH]c(Cl)c1 -1242.448522 0.0028711010236293077 True 0.0 5.709816
mols2grid.display(sdf_format, 
                  # set what's displayed on the grid
                  subset=["parent_id", "img", "tauto_id","E_rel_tauto(kcal/mol)"],
                  # set what's displayed on the hover tooltip
                  tooltip=["parent_id", "E_tot", "fmax", "E_rel_tauto(kcal/mol)"],
                  transform={"E_rel_tauto(kcal/mol)": lambda x: r"del_E_tauto: {0:0.3f} kcal/mol".format(x)},
                  size=(250,250))

Saving and viewing the SD files

Here I am looking at the 3D conformers of the original and tautomers

inf = open(os.path.join(out_folder, 'job1','smi_taut_3d.sdf'),'rb')
with Chem.ForwardSDMolSupplier(inf) as fsuppl:
    ms = [x for x in fsuppl if x is not None]
## Combine all the conformers for a given molecule in 1 molecule object 
mol_dict = []
get_og_name = [x.GetProp('ID').split('@')[0] for x in ms]
for index, row in test_smi.iterrows():
  item_index = []
  for entry_index, og_name in enumerate(get_og_name):
    if str(row['Name']) == og_name:
      item_index.append(entry_index)
  list_sdf = [ms[i] for i in item_index]
  sdf_dict = {}
  sdf_dict['parent'] = row['Name']
  sdf_dict['conformers'] = list_sdf 
  mol_dict.append(sdf_dict)
def to_sdf(mol_dict):
  parent_id = mol_dict['parent']
  w = Chem.SDWriter(f'{out_folder}/conformers_{parent_id}.sdf')
  sdfs = mol_dict['conformers']
  for entries in sdfs:
    w.write(entries)
  w.close()

def append_conformers_to_mol(mol_dict):
  parent_id = mol_dict['parent']
  sdfs = mol_dict['conformers']
  ref = copy.deepcopy(sdfs[0])
  for mol in sdfs:
    conf_mol = mol.GetConformer()
    mol_props = mol.GetPropsAsDict()
    for key, value in mol_props.items():
      conf_mol.SetProp(str(key), str(value))
    ref.AddConformer(conf_mol, assignId=True)
  
  for name in ref.GetPropNames():
    ref.ClearProp(name)
  
  ref.SetProp('_Name',str(parent_id))
  Chem.rdMolAlign.AlignMolConformers(ref)
  return ref 
for mol_list in mol_dict:
  print(mol_list['parent'], len(mol_list['conformers']))
pyridone 2
Cl-pyridone 2
benzene-fused 3
sildenafil 216
boo 10
mol_dict[1]
{'parent': 'Cl-pyridone',
 'conformers': [<rdkit.Chem.rdchem.Mol at 0x2b29365373a0>,
  <rdkit.Chem.rdchem.Mol at 0x2b2936537030>]}
to_sdf(mol_dict[1])
ref = append_conformers_to_mol(mol_dict[-1])
# http://rdkit.blogspot.com/2016/07/using-ipywidgets-and-py3dmol-to-browse.html
import py3Dmol
from rdkit import Chem
from rdkit.Chem import AllChem
from ipywidgets import interact, interactive, fixed
ref.GetNumConformers()
11

What I want to do is generate a set of conformers for a molecule and scroll through them interactively. Here’s some code for doing that:

# https://birdlet.github.io/2019/10/02/py3dmol_example/
# http://rdkit.blogspot.com/2016/07/using-ipywidgets-and-py3dmol-to-browse.html

import py3Dmol

def MolTo3DView(mol, confId, size=(400, 400), style="stick", surface=False, opacity=0.5):
    """Draw molecule in 3D
    
    Args:
    ----
        mol: rdMol, molecule to show
        size: tuple(int, int), canvas size
        style: str, type of drawing molecule
               style can be 'line', 'stick', 'sphere', 'carton'
        surface, bool, display SAS
        opacity, float, opacity of surface, range 0.0-1.0
    Return:
    ----
        viewer: py3Dmol.view, a class for constructing embedded 3Dmol.js views in ipython notebooks.
    """
    assert style in ('line', 'stick', 'sphere', 'carton')
    mblock = Chem.MolToMolBlock(mol, confId=confId)
    viewer = py3Dmol.view(width=size[0], height=size[1])
    viewer.addModel(mblock, 'sdf')
    viewer.setStyle({style:{}})
    if surface:
        viewer.addSurface(py3Dmol.SAS, {'opacity': opacity})
    viewer.zoomTo()
    return viewer
ref.GetProp('_Name')
'boo'
for conf in ref.GetConformers():
    print(conf.GetId())
0
1
2
3
4
5
6
7
8
9
10
from ipywidgets import interact,fixed,IntSlider
import ipywidgets

from rdkit import Chem
from rdkit.Chem import AllChem

def conf_viewer(mol, confId=-1):
    return MolTo3DView(mol, confId).show()

interact(conf_viewer, mol=fixed(ref), confId=ipywidgets.IntSlider(min=0,max=ref.GetNumConformers()-1, step=1))

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
jupyter labextension install jupyterlab_3dmol

<function __main__.conf_viewer(mol, confId=-1)>
sdf_mol_info = sdf_format.loc[ sdf_format['parent_id'] == ref.GetProp('_Name')]
mols2grid.display(sdf_mol_info, 
                  # set what's displayed on the grid
                  subset=["parent_id", "img", "tauto_id","E_rel_tauto(kcal/mol)"],
                  # set what's displayed on the hover tooltip
                  tooltip=["parent_id", "E_tot", "fmax", "E_rel_tauto(kcal/mol)"],
                  transform={"E_rel_tauto(kcal/mol)": lambda x: f'del_E_tauto: {round(x, 3)} kcal/mol'},
                  size=(250,250))