A Multi-Grained Group Symmetric Framework for Learning Protein-Ligand Binding Dynamics

Shengchao Liu, weitao Du, Yanjing Li, Zhuoxinran Li, Vignesh C Bhethanabotla, Nakul Rampal, Omar M. Yaghi, Christian Borgs, Anima Anandkumar, Hongyu Guo, Jennifer T Chayes

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Keywords: protein-ligand binding, group symmetric, multi-grained, molecular simulation, Newtonian dynamics

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TL;DR: We propose a multi-grained geometric neural ODE framework to solve protein-ligand binding dynamics.

Abstract: In drug discovery, molecular dynamics (MD) simulation for protein-ligand binding provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. There has been a long history of improving the efficiency of MD simulations through better numerical methods and, more recently, by augmenting them with machine learning (ML) methods. Yet, challenges remain, such as accurate modeling of extended-timescale simulations. To address this issue, we propose NeuralMD, the first ML surrogate that can facilitate numerical MD and provide accurate simulations of protein-ligand binding dynamics. We propose a principled approach that incorporates a novel physics-informed multi-grained group symmetric framework. Specifically, we propose (1) a BindingNet model that satisfies group symmetry using vector frames and captures the multi-level protein-ligand interactions, and (2) an augmented neural ordinary differential equation solver that learns the trajectory under Newtonian mechanics. For the experiment, we design ten single-trajectory and three multi-trajectory binding simulation tasks. We show the efficiency and effectiveness of NeuralMD, with a 2000$\times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to ~80\% under the stability metric. We further qualitatively show that NeuralMD reaches more stable binding predictions.

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Submission Number: 3731