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J Chem Inf Model

Title:		Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations
Author(s):		Bleiziffer P; Schaller K; Riniker S;
Address:		"Laboratory of Physical Chemistry , ETH Zurich , Vladimir-Prelog-Weg 2 , 8093 Zurich , Switzerland"
Journal Title:		J Chem Inf Model
Year:		2018
Volume:		20180307
Issue:		3
Page Number:		579 - 590
DOI:		10.1021/acs.jcim.7b00663
ISSN/ISBN:		1549-960X (Electronic) 1549-9596 (Linking)
Abstract:		"Parametrization of small organic molecules for classical molecular dynamics simulations is not trivial. The vastness of the chemical space makes approaches using building blocks challenging. The most common approach is therefore an individual parametrization of each compound by deriving partial charges from semiempirical or ab initio calculations and inheriting the bonded and van der Waals (Lennard-Jones) parameters from a (bio)molecular force field. The quality of the partial charges generated in this fashion depends on the level of the quantum-chemical calculation as well as on the extraction procedure used. Here, we present a machine learning (ML) based approach for predicting partial charges extracted from density functional theory (DFT) electron densities. The training set was chosen with the goal to provide a broad coverage of the known chemical space of druglike molecules. In addition to the speed of the approach, the partial charges predicted by ML are not dependent on the three-dimensional conformation in contrast to the ones obtained by fitting to the electrostatic potential (ESP). To assess the quality and compatibility with standard force fields, we performed benchmark calculations for the free energy of hydration and liquid properties such as density and heat of vaporization"
Keywords:		"Electrons *Machine Learning Models, Chemical Molecular Dynamics Simulation Pharmaceutical Preparations/chemistry *Quantum Theory *Static Electricity *Thermodynamics Volatilization;"
Notes:		"MedlineBleiziffer, Patrick Schaller, Kay Riniker, Sereina eng Research Support, Non-U.S. Gov't 2018/02/21 J Chem Inf Model. 2018 Mar 26; 58(3):579-590. doi: 10.1021/acs.jcim.7b00663. Epub 2018 Mar 7"