Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics
- URL: http://arxiv.org/abs/2510.07589v1
- Date: Wed, 08 Oct 2025 22:24:45 GMT
- Title: Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics
- Authors: Juan Viguera Diez, Mathias Schreiner, Simon Olsson,
- Abstract summary: We introduce a deep generative modeling framework that accelerates sampling of molecular dynamics by four orders of magnitude.<n>This approach opens new opportunities for probing conformational landscapes, thermodynamics, and kinetics in systems central to chemistry and biophysics.
- Score: 3.9508022083907393
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conventional molecular dynamics simulations provide atomistic resolution, but their femtosecond time steps limit access to the slow conformational changes and relaxation processes that govern chemical function. Here, we introduce a deep generative modeling framework that accelerates sampling of molecular dynamics by four orders of magnitude while retaining physical realism. Applied to small organic molecules and peptides, the approach enables quantitative characterization of equilibrium ensembles and dynamical relaxation processes that were previously only accessible by costly brute-force simulation. Importantly, the method generalizes across chemical composition and system size, extrapolating to peptides larger than those used for training, and captures chemically meaningful transitions on extended time scales. By expanding the accessible range of molecular motions without sacrificing atomistic detail, this approach opens new opportunities for probing conformational landscapes, thermodynamics, and kinetics in systems central to chemistry and biophysics.
Related papers
- Molecular Representations in Implicit Functional Space via Hyper-Networks [53.70982267248536]
We argue that molecular learning can instead be formulated as learning in function space.<n>We instantiate this formulation with MolField, a hyper-network-based framework that learns distributions over molecular fields.<n>Our results show that treating molecules as continuous functions fundamentally changes how molecular representations generalize across tasks.
arXiv Detail & Related papers (2026-01-29T21:13:37Z) - OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction [63.318434943975255]
We introduce OXtal, a large-scale 100M parameter all-atom diffusion model that learns the conditional joint distribution over intramolecular conformations and periodic packing.<n>By leveraging a large dataset of 600K experimentally validated crystal structures, OXtal achieves orders-of-improvement over prior ab initio machine learning CSP methods.<n> OXtal attains over 80% packing similarity rate, demonstrating its ability to model both thermodynamic and kinetic regularities of molecular crystallization.
arXiv Detail & Related papers (2025-12-07T20:46:30Z) - UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules [19.279397111680115]
We propose textbfUnified bfSimulator (UniSim), which leverages cross-domain knowledge to enhance the understanding of atomic interactions.<n>UniSim achieves highly competitive performance across small molecules, peptides, and proteins.
arXiv Detail & Related papers (2025-05-20T14:29:06Z) - Unveiling the Dance of Molecules: Ro-Vibrational Dynamics of Molecules under Intense Illumination at Complex Plasmonic Interfaces [0.0]
The study investigates relaxation dynamics of an ensemble of molecules following intense resonant pump excitation in Fabry-Perot cavities and at three-dimensional plasmonic metasurfaces.<n>The simulations reveal dramatically modified relaxation pathways inside cavities compared to free space, characterized by persistent molecular alignment.<n>They also indicate the presence of a previously unreported relaxation stabilization mechanism driven by dephasing of the collective molecular-cavity mode.
arXiv Detail & Related papers (2024-12-03T22:17:35Z) - Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics [38.53044197103943]
GF-NODE integrates a graph Fourier transform for spatial frequency decomposition with a Neural ODE framework for continuous-time evolution.<n>We show that GF-NODE achieves state-of-the-art accuracy while preserving essential geometrical features over extended simulations.<n>These findings highlight the promise of bridging spectral decomposition with continuous-time modeling to improve the robustness and predictive power of MD simulations.
arXiv Detail & Related papers (2024-11-03T15:10:48Z) - Machine-learned molecular mechanics force field for the simulation of
protein-ligand systems and beyond [33.54862439531144]
Development of reliable and molecular mechanics (MM) force fields is indispensable for biomolecular simulation and computer-aided drug design.
We introduce a generalized and machine-learned MM force field, ttexttespaloma-0.3, and an end-to-end differentiable framework using graph neural networks.
The force field reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids.
arXiv Detail & Related papers (2023-07-13T23:00:22Z) - Molecule Design by Latent Space Energy-Based Modeling and Gradual
Distribution Shifting [53.44684898432997]
Generation of molecules with desired chemical and biological properties is critical for drug discovery.
We propose a probabilistic generative model to capture the joint distribution of molecules and their properties.
Our method achieves very strong performances on various molecule design tasks.
arXiv Detail & Related papers (2023-06-09T03:04:21Z) - 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) - Bidirectional Generation of Structure and Properties Through a Single
Molecular Foundation Model [44.60174246341653]
We present a novel multimodal molecular pre-trained model that incorporates the modalities of structure and biochemical properties.
Our proposed model pipeline of data handling and training objectives aligns the structure/property features in a common embedding space.
These contributions emerge synergistic knowledge, allowing us to tackle both multimodal and unimodal downstream tasks through a single model.
arXiv Detail & Related papers (2022-11-19T05:16:08Z) - 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) - Learning Neural Generative Dynamics for Molecular Conformation
Generation [89.03173504444415]
We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph.
We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
arXiv Detail & Related papers (2021-02-20T03:17:58Z) - Molecular Latent Space Simulators [8.274472944075713]
We propose latent space simulators (LSS) to learn kinetic models for continuous all-atom simulation trajectories.
We demonstrate the approach in an application to Trp-protein to produce novel ultra-long synthetic folding trajectories.
arXiv Detail & Related papers (2020-07-01T20:05:27Z)
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.