FLOWR.ROOT - A flow matching-based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction.
basic_science · Level V
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- Record sourced from PubMed, PMID 42409794.
- Also identified by DOI 10.1038/s41467-026-74130-9.
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Abstract
We present FLOWR.ROOT, an SE(3)-equivariant flow-matching foundation model that unifies pocket-aware 3D ligand generation with multi-endpoint binding affinity prediction (pIC<sub>50</sub>, pK<sub>i</sub>, pK<sub>d</sub>, pEC<sub>50</sub>) and pLDDT-based confidence estimation in a single backbone. One trained model supports de novo pocket-conditional generation, interaction- and pharmacophore-conditional sampling, scaffold hopping and elaboration, and fragment growing or replacement, enabled by a mixed isotropic-anisotropic prior placement strategy. Training proceeds in three stages: large-scale pre-training on billions of ligand conformations and millions of mixed-fidelity protein-ligand complexes, refinement on curated co-crystal data, and project-specific adaptation via parameter-efficient LoRA finetuning. Joint structure-affinity modelling enables inference-time importance-sampling guidance for single- and multi-objective design without external scoring functions. Case studies on kinase selectivity (CK2α/CLK3) and scaffold elaboration on TYK2, ERα, and BACE1 illustrate utility from hit identification through lead optimization.