FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation
arXiv:2504.10564v3 Announce Type: replace-cross Abstract: We introduce FLOWR, a novel 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 thoroughly curated dataset comprising ligand-pocket co-crystal complexes specifically designed to address existing data quality issues. Empirical evaluations demonstrate that FLOWR surpasses current state-of-the-art diffusion- and flow-
This paper leverages recent advancements in AI, specifically flow matching and equivariant neural networks, to address long-standing challenges in drug discovery at a time of intense computational biology innovation.
A strategic reader should care because this technology represents a significant leap in structure-based drug design, potentially accelerating therapeutic development and reducing R&D costs in pharmaceuticals.
The ability to generate and optimize 3D ligands with higher fidelity and efficiency based on protein pocket conditioning changes the paradigm for early-stage drug discovery, moving beyond traditional screening methods.
- · Pharmaceutical companies
- · Biotech startups
- · Patients with unmet medical needs
- · AI-driven drug discovery platforms
- · Traditional high-throughput screening companies
- · Drug discovery companies slow to adopt AI
More efficient and faster discovery of novel drug candidates for various diseases.
A significant reduction in the average time and cost required to bring a new drug to market, shifting R&D economics.
Enhanced global health outcomes through more targeted and effective therapies, potentially enabling treatments for previously intractable conditions.
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Read at arXiv cs.LG