All-Atom GPCR-Ligand Dynamics Simulation via a Residual Latent Flow Mode
q-bio.QM, cs.AI, cs.LG
Submitted: 2026-02-03
Updated: 2026-09-26
Comments: ICML2026 AI4Science workshop
License: http://creativecommons.org/licenses/by/4.0/
The gist: G-protein-coupled receptors (GPCRs), which are targeted by over one-third of approved drugs, undergo intricate conformational transitions to transduce signals.
Terminology
Abstract
G-protein-coupled receptors (GPCRs), which are targeted by over one-third of approved drugs, undergo intricate conformational transitions to transduce signals. While Molecular Dynamics (MD) is essential for elucidating this transduction process, particularly within ligand-bound complexes, conventional all-atom MD simulation is computationally prohibitive. In this paper, we introduce GPCRLMD, a deep generative framework for efficient all-atom GPCR-ligand simulation. GPCRLMD employs an Atom-Anchored Thermal Variational Autoencoder (AAT-VAE) to first map the complex into a regularized dimension-preserving latent space, maintaining geometric topology via physics-informed constraints. Within this latent space, a Residual Latent Flow samples evolution trajectories, which are subsequently decoded back to atomic coordinates. By capturing temporal dynamics via relative displacements anchored to the initial structure, this residual mechanism effectively decouples static topology from dynamic fluctuations. Experimental results demonstrate that GPCRLMD achieves state-of-the-art performance in GPCR-ligand dynamics simulation, faithfully reproducing ensemble observables and critical ligand-receptor interactions.
Sources
- La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching
- On the Relation between Rectified Flows and Optimal Transport
- A Novel 4-D Dataset Paradigm for Studying Complete Ligand-Protein Dissociation Dynamics
- Efficient Flow Matching using Latent Variables
- Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings
- ConfRover: Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression
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