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J Chem Inf Model. 2019 Aug 26;59(8):3485-3493. doi: 10.1021/acs.jcim.9b00439. Epub 2019 Aug 8.

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

Author information

1
Acellera Labs , C/Doctor Trueta 183 , 08005 Barcelona , Spain.
2
Computational Science Laboratory , Universitat Pompeu Fabra , PRBB, C/Doctor Aiguader 88 , 08003 Barcelona , Spain.
3
Instituci├│ Catalana de Recerca i Estudis Avan├žats (ICREA) , Passeig Lluis Companys 23 , 08010 Barcelona , Spain.

Abstract

Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules. Here, we present an automated FF parameterization method which can utilize either density functional theory (DFT) calculations or approximate QM energies produced by different neural network potentials (NNPs), to obtain improved parameters for molecules. We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2). We expect our method to be of critical importance in computational structure-based drug discovery (SBDD). The current version is available at PlayMolecule ( www.playmolecule.org ) and implemented in HTMD, allowing to parameterize molecules with different QM and NNP options.

PMID:
31322877
DOI:
10.1021/acs.jcim.9b00439

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