A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
CoRR(2024)
摘要
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 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× 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 compared to other machine learning methods.
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