ArticleNature computational science2026
FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation.
Article in Nature computational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Is AI Capable of Real-World Drug Discovery?Journal of chemical information and modeling · 2026Article
- Controllable molecular generation with fine-tuned flow-matching model.Communications chemistry · 2026Article
- FLOWR.ROOT - A flow matching-based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction.Nature communications · 2026Article
- Assessing the factors influencing the quality of pocket-conditioned 3D generative models.Journal of cheminformatics · 2026Article
- FlowMol3: flow matching for 3DDigital discovery · 2026Article
- Improving protein-ligand complex generation with force field guidance.Journal of cheminformatics · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
- Article
- Assay2Mol: Large Language Model-based Drug Design Using BioAssay Context.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Here we introduce FLOWR, a structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching with equivariant optimal transport, enhanced by an efficient protein pocket conditioning. Alongside FLOWR, we present SPINDR, a curated dataset comprising ligand-pocket cocrystal complexes specifically designed to address existing data quality issues. Empirical evaluations demonstrate that FLOWR surpasses current state-of-the-art diffusion- and flow-based methods in terms of PoseBusters-validity, pose accuracy and interaction recovery, while offering an inference speed-up, achieving up to 70-fold faster performance. In addition, we introduce FLOWR.MULTI, a highly accurate multi-purpose model allowing for the targeted sampling of ligands that adhere to predefined interaction profiles and chemical substructures for fragment-based design without the need of retraining or any resampling strategies. Collectively, our results indicate that FLOWR and FLOWR.MULTI represent an advancement in artificial intelligence-driven structure-based drug design, substantially enhancing the reliability and applicability of de novo, interaction- and fragment-based ligand generation in real-world drug discovery settings.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.