Fast and accurate generation of ab initio quality atomic charges using nonparametric statistical regression

Abstract

We introduce a class of partial atomic charge assignment method that provides ab initio quality description of the electrostatics of bioorganic molecules. The method uses a set of models that neither have a fixed functional form nor require a fixed set of parameters, and therefore are capable of capturing the complexities of the charge distribution in great detail. Random Forest regression is used to build separate charge models for elements H, C, N, O, F, S, and Cl, using training data consisting of partial charges along with a description of their surrounding chemical environments; training set charges are generated by fitting to the b3lyp/6-31G* electrostatic potential (ESP) and are subsequently refined to improve consistency and transferability of the charge assignments. Using a set of 210 neutral, small organic molecules, the absolute hydration free energy calculated using these charges in conjunction with Generalized Born solvation model shows a low mean unsigned error, close to 1 kcal/mol, from the experimental data. Using another large and independent test set of chemically diverse organic molecules, the method is shown to accurately reproduce charge-dependent observables--ESP and dipole moment--from ab initio calculations. The method presented here automatically provides an estimate of potential errors in the charge assignment, enabling systematic improvement of these models using additional data. This work has implications not only for the future development of charge models but also in developing methods to describe many other chemical properties that require accurate representation of the electronic structure of the system.

A GROMOS-Compatible Force Field for Small Organic Molecules in the Condensed Phase: The 2016H66 Parameter Set

“…Importantly, 2016H66 also addresses point 9. In many current small-molecule force fields 11,22,43,44,252,253,256 , QM-derived (thus also in principle structure-dependent) atomic charges are used along within standardized van der Waals atom-type sets, a situation that is somewhat reminiscent of the separate optimization of van der Waals parameters and charges in the design of the 53A6 set 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 dynamics (time-dependence) of the system, whereas D and η are transport parameters which characterize precisely this dynamics. The dynamic information is thus implicitly added by the physical model.…”

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

“…With ML we refer to statistical algorithms that extract correlations by training on input/output data, and that improve in predictive power as more training data is added [18]. While ML models for the fitting of potential energies have been in use for decades [19], the possibility to infer point charges, MTPs and polarizabilities has been investigated only recently [20][21][22][23]. These approaches interpolate between a large number of conformations to accurately describe the effects of changes in the geometry.…”

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

“…Although several alternative methods of charge derivation have been proposed, 47,53,60,62,63 restraining the charges of buried atoms to prevent the optimization from converging toward unreasonable values and/or to reduce conformational dependence of the charges became the most popular in force field development. [64][65][66][67][68][69][70][71][72][73][74][75][76][77] In most of these methods, besides a constraint on the total charge of the molecule, an additional restraining function is added to the LS sum (eq 1) to keep the buried atom charges close to some predefined values, despite its possible negative effect on the dipole moment values and the overall quality of MEP. 53,78 Considering the challenges presented by the relatively straightforward single-objective point charge fitting against the MEP, simultaneous optimization of point charges along with other force field parameters against a diverse training set could be expected to present even more pitfalls.…”