EvoEGF-Mol: Evolving Exponential Geodesic Flow for Structure-based Drug Design

• URL: http://arxiv.org/abs/2601.22466v1
• Date: Fri, 30 Jan 2026 02:26:13 GMT
• Title: EvoEGF-Mol: Evolving Exponential Geodesic Flow for Structure-based Drug Design
• Authors: Yaowei Jin, Junjie Wang, Cheng Cao, Penglei Wang, Duo An, Qian Shi,
• Abstract summary: We propose an information-geometric approach to structure-based drug design.<n>EvoEGF-Mol replaces static Dirac targets with dynamically concentrating distributions.<n>Our model approaches a reference-level PoseBusters passing rate (93.4%) on CrossDock, demonstrating remarkable precision and interaction fidelity.
• Score: 5.680996830009093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structure-Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean and probabilistic spaces for continuous atomic coordinates and discrete chemical categories, leading to a mismatch with the underlying statistical manifolds. We address this issue from an information-geometric perspective by modeling molecules as composite exponential-family distributions and defining generative flows along exponential geodesics under the Fisher-Rao metric. To avoid the instantaneous trajectory collapse induced by geodesics directly targeting Dirac distributions, we propose Evolving Exponential Geodesic Flow for SBDD (EvoEGF-Mol), which replaces static Dirac targets with dynamically concentrating distributions, ensuring stable training via a progressive-parameter-refinement architecture. Our model approaches a reference-level PoseBusters passing rate (93.4%) on CrossDock, demonstrating remarkable geometric precision and interaction fidelity, while outperforming baselines on real-world MolGenBench tasks by recovering bioactive scaffolds and generating candidates that meet established MedChem filters.

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