Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation
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 conditi
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 58%Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language →
- PossiblePossibly related (embedding) · 57%Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1 →
- PossiblePossibly related (embedding) · 53%How a Google DeepMind Spin-off Hunts Hidden Drug Targets →
- PossiblePossibly related (embedding) · 51%How AI helps scientists design the next generation of medicines →
- LinkedLinked via arxiv author · 85%Tianyu Gao →
“Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation”
- LinkedLinked via arxiv author · 85%Zhikai Su →
“Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation”
- LinkedLinked via arxiv author · 85%Jiashu Li →
“Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation”
- LinkedLinked via arxiv author · 85%Wenjun Gao →
“Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation”