Literature DB >> 34551262

Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field.

Yudong Qiu1, Daniel G A Smith2, Simon Boothroyd3, Hyesu Jang1, David F Hahn4, Jeffrey Wagner5, Caitlin C Bannan5,6, Trevor Gokey5, Victoria T Lim5, Chaya D Stern3, Andrea Rizzi3,7, Bryon Tjanaka5, Gary Tresadern4, Xavier Lucas8, Michael R Shirts9, Michael K Gilson6, John D Chodera3, Christopher I Bayly10, David L Mobley5, Lee-Ping Wang1.   

Abstract

We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.

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Year:  2021        PMID: 34551262      PMCID: PMC8511297          DOI: 10.1021/acs.jctc.1c00571

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.578


  58 in total

1.  Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine.

Authors:  Ajay N Jain
Journal:  J Med Chem       Date:  2003-02-13       Impact factor: 7.446

2.  Development and testing of a general amber force field.

Authors:  Junmei Wang; Romain M Wolf; James W Caldwell; Peter A Kollman; David A Case
Journal:  J Comput Chem       Date:  2004-07-15       Impact factor: 3.376

3.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

Authors:  Richard A Friesner; Jay L Banks; Robert B Murphy; Thomas A Halgren; Jasna J Klicic; Daniel T Mainz; Matthew P Repasky; Eric H Knoll; Mee Shelley; Jason K Perry; David E Shaw; Perry Francis; Peter S Shenkin
Journal:  J Med Chem       Date:  2004-03-25       Impact factor: 7.446

4.  OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins.

Authors:  Edward Harder; Wolfgang Damm; Jon Maple; Chuanjie Wu; Mark Reboul; Jin Yu Xiang; Lingle Wang; Dmitry Lupyan; Markus K Dahlgren; Jennifer L Knight; Joseph W Kaus; David S Cerutti; Goran Krilov; William L Jorgensen; Robert Abel; Richard A Friesner
Journal:  J Chem Theory Comput       Date:  2015-12-01       Impact factor: 6.006

5.  Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery.

Authors:  Santiago Vilar; Giorgio Cozza; Stefano Moro
Journal:  Curr Top Med Chem       Date:  2008       Impact factor: 3.295

6.  Driving torsion scans with wavefront propagation.

Authors:  Yudong Qiu; Daniel G A Smith; Chaya D Stern; Mudong Feng; Hyesu Jang; Lee-Ping Wang
Journal:  J Chem Phys       Date:  2020-06-28       Impact factor: 3.488

7.  All-atom empirical potential for molecular modeling and dynamics studies of proteins.

Authors:  A D MacKerell; D Bashford; M Bellott; R L Dunbrack; J D Evanseck; M J Field; S Fischer; J Gao; H Guo; S Ha; D Joseph-McCarthy; L Kuchnir; K Kuczera; F T Lau; C Mattos; S Michnick; T Ngo; D T Nguyen; B Prodhom; W E Reiher; B Roux; M Schlenkrich; J C Smith; R Stote; J Straub; M Watanabe; J Wiórkiewicz-Kuczera; D Yin; M Karplus
Journal:  J Phys Chem B       Date:  1998-04-30       Impact factor: 2.991

8.  Geometry optimization made simple with translation and rotation coordinates.

Authors:  Lee-Ping Wang; Chenchen Song
Journal:  J Chem Phys       Date:  2016-06-07       Impact factor: 4.304

9.  Benchmark assessment of molecular geometries and energies from small molecule force fields.

Authors:  Victoria T Lim; David F Hahn; Gary Tresadern; Christopher I Bayly; David L Mobley
Journal:  F1000Res       Date:  2020-12-03

10.  Ligand pose and orientational sampling in molecular docking.

Authors:  Ryan G Coleman; Michael Carchia; Teague Sterling; John J Irwin; Brian K Shoichet
Journal:  PLoS One       Date:  2013-10-01       Impact factor: 3.240

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  16 in total

1.  Integration of Experimental Data and Use of Automated Fitting Methods in Developing Protein Force Fields.

Authors:  Marcelo D Polêto; Justin A Lemkul
Journal:  Commun Chem       Date:  2022-03-18

2.  High Order Ab Initio Valence Force Field with Chemical Pattern Based Parameter Assignment.

Authors:  Xudong Yang; Chengwen Liu; Pengyu Ren
Journal:  J Comput Biophys Chem       Date:  2021-12-29

3.  Preserving the Integrity of Empirical Force Fields.

Authors:  Asuka A Orr; Suliman Sharif; Junmei Wang; Alexander D MacKerell
Journal:  J Chem Inf Model       Date:  2022-08-02       Impact factor: 6.162

4.  Automation of AMOEBA polarizable force field for small molecules: Poltype 2.

Authors:  Brandon Walker; Chengwen Liu; Elizabeth Wait; Pengyu Ren
Journal:  J Comput Chem       Date:  2022-07-01       Impact factor: 3.672

5.  Expanded Ensemble Methods Can be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies.

Authors:  Si Zhang; David F Hahn; Michael R Shirts; Vincent A Voelz
Journal:  J Chem Theory Comput       Date:  2021-09-13       Impact factor: 6.578

6.  Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo.

Authors:  Yunhui Ge; Oliver J Melling; Weiming Dong; Jonathan W Essex; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2022-10-06       Impact factor: 4.179

7.  Improving Force Field Accuracy by Training against Condensed-Phase Mixture Properties.

Authors:  Simon Boothroyd; Owen C Madin; David L Mobley; Lee-Ping Wang; John D Chodera; Michael R Shirts
Journal:  J Chem Theory Comput       Date:  2022-05-09       Impact factor: 6.578

8.  Replica-Exchange Enveloping Distribution Sampling Using Generalized AMBER Force-Field Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules.

Authors:  Salomé R Rieder; Benjamin Ries; Kay Schaller; Candide Champion; Emilia P Barros; Philippe H Hünenberger; Sereina Riniker
Journal:  J Chem Inf Model       Date:  2022-06-08       Impact factor: 6.162

Review 9.  Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective.

Authors:  Katya Ahmad; Andrea Rizzi; Riccardo Capelli; Davide Mandelli; Wenping Lyu; Paolo Carloni
Journal:  Front Mol Biosci       Date:  2022-06-08

10.  Generalizing the Discrete Gibbs Sampler-Based λ-Dynamics Approach for Multisite Sampling of Many Ligands.

Authors:  Jonah Z Vilseck; Xinqiang Ding; Ryan L Hayes; Charles L Brooks
Journal:  J Chem Theory Comput       Date:  2021-06-08       Impact factor: 6.006

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