Abstract:
:Two different types of approaches: (a) approaches that combine quantitative structure activity relationships, quantum mechanical electronic structure methods, and machine-learning and, (b) electronic structure vertical solvation approaches, were used to predict the logP coefficients of 11 molecules as part of the SAMPL6 logP blind prediction challenge. Using electronic structures optimized with density functional theory (DFT), several molecular descriptors were calculated for each molecule, including van der Waals areas and volumes, HOMO/LUMO energies, dipole moments, polarizabilities, and electrophilic and nucleophilic superdelocalizabilities. A multilinear regression model and a partial least squares model were used to train a set of 97 molecules. As well, descriptors were generated using the molecular operating environment and used to create additional machine learning models. Electronic structure vertical solvation approaches considered include DFT and the domain-based local pair natural orbital methods combined with the solvated variant of the correlation consistent composite approach.
journal_name
J Comput Aided Mol Desjournal_title
Journal of computer-aided molecular designauthors
Patel P,Kuntz DM,Jones MR,Brooks BR,Wilson AKdoi
10.1007/s10822-020-00287-0subject
Has Abstractpub_date
2020-05-01 00:00:00pages
495-510issue
5eissn
0920-654Xissn
1573-4951pii
10.1007/s10822-020-00287-0journal_volume
34pub_type
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