Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials

被引：14

作者：

Zariquiey, Francesc Sabanes ^{[1
,2
]}

Galvelis, Raimondas ^{[1
,2
]}

Gallicchio, Emilio ^{[3
]}

Chodera, John D. ^{[4
]}

Markland, Thomas E. ^{[5
]}

De Fabritiis, Gianni ^{[1
,2
,6
]}

机构：

[1] Univ Pompeu Fabra, Computat Sci Lab, Barcelona 08003, Spain

[2] Acellera Labs, Barcelona 08005, Spain

[3] CUNY Brooklyn Coll, Grad Ctr, Dept Chem, New York, NY 11210 USA

[4] Mem Sloan Kettering Canc Ctr, Sloan Kettering Inst, Computat & Syst Biol Program, New York, NY 10065 USA

[5] Stanford Univ, Dept Chem, Stanford, CA 94305 USA

[6] Inst Catalana Recerca & Estudis Avancats ICREA, Barcelona 08010, Spain

来源：

JOURNAL OF CHEMICAL INFORMATION AND MODELING | 2024年 / 64卷 / 05期

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

GENERAL FORCE-FIELD; PARAMETERS;

D O I：

10.1021/acs.jcim.3c02031

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.

引用

页码：1481 / 1485

页数：5

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