Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation

2026-08-31

AI Summary

本文提出LiFT框架，通过语言信息指导3D分子生成，解决结构基础药物设计中3D目标亲和力与1D化学有效性的问题。

Abstract

Structure-based drug design (SBDD) requires ligands that satisfy both 3D target affinity and 1D chemical validity. Existing controllable generation methods often rely on task-specific fine-tuning or externally imposed sampling-time guidance, adding cost and potentially conflicting with evolving 3D geometric constraints. We propose LiFT, a language-informed cross-modal framework built on Flow Matching for trend-guided 3D molecular generation across both de novo design and scaffold hopping. LiFT uses a "Sense-Evolve-Assemble" agent to generate target-aware SMILES as intermediate chemical conditions, from which a pre-trained chemical foundation model extracts continuous semantic priors. These priors are integrated into geometric generation through a lightweight semantic projector with zero-initialized adaptive normalization for stable cross-modal conditioning. We further introduce a Self-Conditioned Decoupled Router (SCDR), which modulates the velocity field according to intermediate structural states during ODE integration. Experiments on Cross-Docked2020 show that LiFT achieves competitive distribution matching while improving medicinal chemistry metrics and maintaining competitive structural validity under task-steering settings without additional generator fine-tuning. Our results suggest that language-derived chemical priors provide effective trend-level guidance for 3D molecular generation. Code and released artifacts are available at https://github.com/kasurl/LiFT.

Problem

• structure-based drug design
• 3D target affinity
• 1D chemical validity
• controllable generation methods
• geometric constraints

Innovation

• language-informed
• Flow Matching
• Sense-Evolve-Assemble agent
• continuous semantic priors
• Self-Conditioned Decoupled Router (SCDR)