PINN-MEP: Continuous Neural Representations for Minimum-Energy Path Discovery in Molecular Systems

2025-04-23

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

• Machine Learning: Bio-inspired Learning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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

• 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

• Physics-informed neural networks for path optimization
• Implicit neural functions represent transition paths
• Differentiable force fields enable efficient pathway discovery