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

• URL: http://arxiv.org/abs/2608.31009v1
• Date: Mon, 31 Aug 2026 15:57:09 GMT
• Title: Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation
• Abstract summary: Structure-based drug design (SBDD) requires that satisfy both 3D target affinity and 1D chemical validity.<n>Existing controllable generation methods often rely on task-specific fine-tuning or externally imposed geometric sampling-time guidance.<n>We propose LiFT, a language-informed cross-modal framework built on Flow Matching for trend-guided 3D molecular generation.
• Score: 12.578501426205792
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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