The emergence of machine learning force fields in drug design

Author:

Chen Mingan123ORCID,Jiang Xinyu14,Zhang Lehan14,Chen Xiaoxu145,Wen Yiming145,Gu Zhiyong145,Li Xutong14,Zheng Mingyue145ORCID

Affiliation:

1. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai China

2. School of Physical Science and Technology ShanghaiTech University Shanghai China

3. Lingang Laboratory Shanghai China

4. School of Pharmacy University of Chinese Academy of Sciences Beijing China

5. School of Pharmaceutical Science and Technology Hangzhou Institute for Advanced Study, UCAS Hangzhou China

Abstract

AbstractIn the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high‐quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.

Publisher

Wiley

Subject

Drug Discovery,Pharmacology,Molecular Medicine

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