Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations

2024/08/17 by Gianni De Fabritiis, De Fabritiis, Gianni · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Genetics, Bioinformatics, and Biomedical Research #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods

paper · pdf · doi:10.48550/arxiv.2408.12625

Abstract

Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore their potential to dramatically improve accuracy and scalability in simulating complex molecular systems. I discuss key challenges that must be addressed to fully realize their transformative potential in chemical biology and related fields.

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• AIQM2: organic reaction simulations beyond DFT
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