Enhanced Sampling, Public Dataset and Generative Model for Drug-Protein Dissociation Dynamics

• URL: http://arxiv.org/abs/2504.18367v1
• Date: Fri, 25 Apr 2025 14:10:06 GMT
• Title: Enhanced Sampling, Public Dataset and Generative Model for Drug-Protein Dissociation Dynamics
• Authors: Maodong Li, Jiying Zhang, Bin Feng, Wenqi Zeng, Dechin Chen, Zhijun Pan, Yu Li, Zijing Liu, Yi Isaac Yang,
• Abstract summary: Drug-protein binding and dissociation dynamics are fundamental to understanding molecular interactions in biological systems.<n>We propose a novel research paradigm that combines molecular dynamics simulations, enhanced sampling, and AI generative models to address this issue.<n>Our ongoing efforts focus on expanding this methodology to encompass a broader spectrum of drug-protein complexes and exploring novel applications in pathway prediction.
• Score: 10.80659641278556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Drug-protein binding and dissociation dynamics are fundamental to understanding molecular interactions in biological systems. While many tools for drug-protein interaction studies have emerged, especially artificial intelligence (AI)-based generative models, predictive tools on binding/dissociation kinetics and dynamics are still limited. We propose a novel research paradigm that combines molecular dynamics (MD) simulations, enhanced sampling, and AI generative models to address this issue. We propose an enhanced sampling strategy to efficiently implement the drug-protein dissociation process in MD simulations and estimate the free energy surface (FES). We constructed a program pipeline of MD simulations based on this sampling strategy, thus generating a dataset including 26,612 drug-protein dissociation trajectories containing about 13 million frames. We named this dissociation dynamics dataset DD-13M and used it to train a deep equivariant generative model UnbindingFlow, which can generate collision-free dissociation trajectories. The DD-13M database and UnbindingFlow model represent a significant advancement in computational structural biology, and we anticipate its broad applicability in machine learning studies of drug-protein interactions. Our ongoing efforts focus on expanding this methodology to encompass a broader spectrum of drug-protein complexes and exploring novel applications in pathway prediction.

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