Technology Category
Machine Learning: Bio-inspired LearningSearch and Optimization: Sampling/Simulation-based SearchPlanning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
PINN-MEP: Continuous Neural Representations for Minimum-Energy Path Discovery in Molecular Systems
🤖 AI Summary
Rare-event conformational transitions—such as ion channel gating—in molecular systems exhibit high energy barriers, rendering them poorly sampled by conventional molecular dynamics (MD) or Markov chain Monte Carlo (MCMC) methods. To address this, we propose an end-to-end, physics-informed neural network (PINN)-based continuous optimization framework for computing minimum energy paths (MEPs), the first to integrate PINNs with the string method philosophy. Our approach eliminates the need for an initial path guess and avoids explicit path sampling and reparameterization. Leveraging automatic differentiation, differentiable force fields, and an implicit neural representation of the reaction path, it scales to large, explicit-solvent biomolecular systems (>8,300 atoms). Applied to proteins including BPTI, the computed MEPs are physically realistic and quantitatively accurate. Computational efficiency exceeds that of conventional string methods by over one order of magnitude, establishing a new, scalable paradigm for discovering transition pathways in complex biomolecules.
Technology Category
Application Category
📝 Abstract
Characterizing conformational transitions in physical systems remains a fundamental challenge in the computational sciences. Traditional sampling methods like molecular dynamics (MD) or MCMC often struggle with the high-dimensional nature of molecular systems and the high energy barriers of transitions between stable states. While these transitions are rare events in simulation timescales, they often represent the most biologically significant processes - for example, the conformational change of an ion channel protein from its closed to open state, which controls cellular ion flow and is crucial for neural signaling. Such transitions in real systems may take milliseconds to seconds but could require months or years of continuous simulation to observe even once. We present a method that reformulates transition path generation as a continuous optimization problem solved through physics-informed neural networks (PINNs) inspired by string methods for minimum-energy path (MEP) generation. By representing transition paths as implicit neural functions and leveraging automatic differentiation with differentiable molecular dynamics force fields, our method enables the efficient discovery of physically realistic transition pathways without requiring expensive path sampling. We demonstrate our method's effectiveness on two proteins, including an explicitly hydrated bovine pancreatic trypsin inhibitor (BPTI) system with over 8,300 atoms.
Problem
Research questions and friction points this paper is trying to address.
Discovering minimum-energy paths in molecular systems efficiently
Overcoming high energy barriers in molecular conformational transitions
Replacing expensive sampling with neural network-based optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Physics-informed neural networks for path optimization
Implicit neural functions represent transition paths
Differentiable force fields enable efficient pathway discovery
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