Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm

• URL: http://arxiv.org/abs/2511.06585v1
• Date: Mon, 10 Nov 2025 00:24:06 GMT
• Title: Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm
• Authors: Aaryesh Deshpande,
• Abstract summary: Physics-informed machine learning offers a systematic framework that integrates data-driven inference with physical constraints.<n>We frame these approaches as solutions to the "biomolecular closure problem"<n>We outline prospective research avenues at the intersection of machine learning, statistical physics, and computational chemistry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The convergence of statistical learning and molecular physics is transforming our approach to modeling biomolecular systems. Physics-informed machine learning (PIML) offers a systematic framework that integrates data-driven inference with physical constraints, resulting in models that are accurate, mechanistic, generalizable, and able to extrapolate beyond observed domains. This review surveys recent advances in physics-informed neural networks and operator learning, differentiable molecular simulation, and hybrid physics-ML potentials, with emphasis on long-timescale kinetics, rare events, and free-energy estimation. We frame these approaches as solutions to the "biomolecular closure problem", recovering unresolved interactions beyond classical force fields while preserving thermodynamic consistency and mechanistic interpretability. We examine theoretical foundations, tools and frameworks, computational trade-offs, and unresolved issues, including model expressiveness and stability. We outline prospective research avenues at the intersection of machine learning, statistical physics, and computational chemistry, contending that future advancements will depend on mechanistic inductive biases, and integrated differentiable physical learning frameworks for biomolecular simulation and discovery.

Related papers

• Foundation Models for Discovery and Exploration in Chemical Space [57.97784111110166]
  MIST is a family of molecular foundation models trained on large unlabeled datasets.<n>We demonstrate the ability of these models to solve real-world problems across chemical space.
  arXiv  Detail & Related papers  (2025-10-20T17:56:01Z)
• Physics-Informed Machine Learning in Biomedical Science and Engineering [3.87707864695882]
  Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems.<n>We review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs)
  arXiv  Detail & Related papers  (2025-10-06T22:52:39Z)
• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics [5.5371760658918]
  Integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems.<n>This perspective highlights advances and potential of physics-driven learning methods.<n>It is shown that the fusion of ML and physics can lead to more efficient and reliable problem-solving strategies.
  arXiv  Detail & Related papers  (2025-01-09T21:14:25Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Symmetry-invariant quantum machine learning force fields [0.0]
  We design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries. Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning.
  arXiv  Detail & Related papers  (2023-11-19T16:15:53Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Constructing Custom Thermodynamics Using Deep Learning [10.008895786910195]
  One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data. Here we develop a platform based on a generalized Onsager principle to learn macroscopic descriptions of arbitrary dissipative systems. We demonstrate its effectiveness by studying theoretically and experimentally validating the stretching of long polymer chains in an externally applied field.
  arXiv  Detail & Related papers  (2023-08-08T08:19:43Z)
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning [60.02391969049972]
  We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
  arXiv  Detail & Related papers  (2023-06-08T17:12:08Z)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning [0.0]
  Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science. In settings involving many simulations, machine learning can reduce these costs, often by orders of magnitude, by interpolating between reference simulations. We comprehensively review and discuss current representations and relations between them, using a unified mathematical framework.
  arXiv  Detail & Related papers  (2020-03-26T18:01:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.