TY - JOUR
T1 - MolTaut
T2 - A Tool for the Rapid Generation of Favorable Tautomer in Aqueous Solution
AU - Pan, Xiaolin
AU - Zhao, Fanyu
AU - Zhang, Yueqing
AU - Wang, Xingyu
AU - Xiao, Xudong
AU - Zhang, John Z.H.
AU - Ji, Changge
N1 - Publisher Copyright:
© 2023 American Chemical Society
PY - 2023/4/10
Y1 - 2023/4/10
N2 - Fast and proper treatment of the tautomeric states for drug-like molecules is critical in computer-aided drug discovery since the major tautomer of a molecule determines its pharmacophore features and physical properties. We present MolTaut, a tool for the rapid generation of favorable states of drug-like molecules in water. MolTaut works by enumerating possible tautomeric states with tautomeric transformation rules, ranking tautomers with their relative internal energies and solvation energies calculated by AI-based models, and generating preferred ionization states according to predicted microscopic pKa. Our test shows that the ranking ability of the AI-based tautomer scoring approach is comparable to the DFT method (wB97X/6-31G*//M062X/6-31G*/SMD) from which the AI models try to learn. We find that the substitution effect on tautomeric equilibrium is well predicted by MolTaut, which is helpful in computer-aided ligand design. The source code of MolTaut is freely available to researchers and can be accessed at https://github.com/xundrug/moltaut. To facilitate the usage of MolTaut by medicinal chemists, we made a free web server, which is available at http://moltaut.xundrug.cn. MolTaut is a handy tool for investigating the tautomerization issue in drug discovery.
AB - Fast and proper treatment of the tautomeric states for drug-like molecules is critical in computer-aided drug discovery since the major tautomer of a molecule determines its pharmacophore features and physical properties. We present MolTaut, a tool for the rapid generation of favorable states of drug-like molecules in water. MolTaut works by enumerating possible tautomeric states with tautomeric transformation rules, ranking tautomers with their relative internal energies and solvation energies calculated by AI-based models, and generating preferred ionization states according to predicted microscopic pKa. Our test shows that the ranking ability of the AI-based tautomer scoring approach is comparable to the DFT method (wB97X/6-31G*//M062X/6-31G*/SMD) from which the AI models try to learn. We find that the substitution effect on tautomeric equilibrium is well predicted by MolTaut, which is helpful in computer-aided ligand design. The source code of MolTaut is freely available to researchers and can be accessed at https://github.com/xundrug/moltaut. To facilitate the usage of MolTaut by medicinal chemists, we made a free web server, which is available at http://moltaut.xundrug.cn. MolTaut is a handy tool for investigating the tautomerization issue in drug discovery.
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U2 - 10.1021/acs.jcim.2c01393
DO - 10.1021/acs.jcim.2c01393
M3 - Article
C2 - 36939644
AN - SCOPUS:85151268926
SN - 1549-9596
VL - 63
SP - 1833
EP - 1840
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 7
ER -