Work

FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation

Year: 2026

Type: article

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.

Source: Nature Computational Science

DOI: 10.1038/s43588-026-00998-8

Authors: Julian Cremer, Ross Irwin, Alessandro Tibo, Jon Paul Janet, Simon Olsson, Djork-Arné Clevert

Institutions: Pfizer (Germany), AstraZeneca (Sweden), Chalmers University of Technology, University of Gothenburg

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $12,690

Funders: Knut och Alice Wallenbergs Stiftelse, Vetenskapsrådet