Energy-Guided Flow Matching Enables Few-Step Conformer Generation and Ground-State Identification

• URL: http://arxiv.org/abs/2512.22597v1
• Date: Sat, 27 Dec 2025 14:00:22 GMT
• Title: Energy-Guided Flow Matching Enables Few-Step Conformer Generation and Ground-State Identification
• Authors: Guikun Xu, Xiaohan Yi, Peilin Zhao, Yatao Bian,
• Abstract summary: We present EnFlow, a unified framework that couples flow matching with an explicitly learned energy model.<n>By incorporating energy-gradient guidance during sampling, our method steers trajectories toward lower-energy regions.<n>The learned energy function further enables efficient energy-based ranking of generated ensembles for accurate ground-state identification.
• Score: 45.52894539097255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating low-energy conformer ensembles and identifying ground-state conformations from molecular graphs remain computationally demanding with physics-based pipelines. Current learning-based approaches often suffer from a fragmented paradigm: generative models capture diversity but lack reliable energy calibration, whereas deterministic predictors target a single structure and fail to represent ensemble variability. Here we present EnFlow, a unified framework that couples flow matching (FM) with an explicitly learned energy model through an energy-guided sampling scheme defined along a non-Gaussian FM path. By incorporating energy-gradient guidance during sampling, our method steers trajectories toward lower-energy regions, substantially improving conformational fidelity, particularly in the few-step regime. The learned energy function further enables efficient energy-based ranking of generated ensembles for accurate ground-state identification. Extensive experiments on GEOM-QM9 and GEOM-Drugs demonstrate that EnFlow simultaneously improves generation metrics with 1--2 ODE-steps and reduces ground-state prediction errors compared with state-of-the-art methods.

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