Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Asahi Kasei Pharma Corporation（旭化成制药株式会社）；University of California, Irvine（加州大学欧文分校）；University of California San Diego（加州大学圣迭戈分校）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Open Molecular Software Foundation（开放分子软件基金会）
Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara, Chapin E. Cavender, Anika J. Friedman, Michael M. Henry, Hugo MacDermott Opeskin, Christopher R. Iacovella, A… 展开作者
Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara, Chapin E. Cavender, Anika J. Friedman, Michael M. Henry, Hugo MacDermott Opeskin, Christopher R. Iacovella, Arnav M. Nagle, Alexander Matthew Payne, Michael R. Shirts, David L. Mobley, John D. Chodera, Yuanqing Wang
The development of reliable and extensible molecular mechanics (MM) force fields -- fast, empirical models characterizing the potential energy surface of molecular systems -- is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, \texttt{espaloma-0.3}, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, \texttt{espaloma-0.3} reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.
One-step Structure Prediction and Screening for Protein-Ligand Complexes using Multi-Task Geometric Deep Learning
Medical School of Nanjing University（南京大学医学院）；National Institute of Healthcare Data Science, Nanjing University（南京大学国家健康数据科学研究院）；School of Life Sciences, Nanjing University（南京大学生命科学学院）
Predictable Accuracy of Martini 3 Absolute Protein-Ligand Binding Free Energies across 15 Targets
Institute for Theoretical Physics, Heidelberg University（海德堡大学理论物理研究所）；Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University（海德堡大学跨学科科学计算中心）
Machine Learning Scoring Functions for Drug Discoveries from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions
An evaluation of machine learning/molecular mechanics end-state corrections with mechanical embedding to calculate relative protein-ligand binding free energies
Accelerated descriptor-free path sampling for protein-ligand binding kinetics
Goethe University Frankfurt（法兰克福歌德大学）；Institute for Computer Science, Goethe University Frankfurt（法兰克福歌德大学计算机研究所）；Frankfurt Institute for Advanced Studies (FIAS), Goethe University Frankfurt（法兰克福高等研究院）
Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes
Cleveland Clinic Foundation（克利夫兰诊所基金会）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；IBM Quantum, IBM Research – Tokyo（IBM量子，IBM研究-东京）；IBM Quantum, T.J. Watson Research Center（IBM量子，T.J.沃森研究中心）
