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 (pIC50, pKi, pKd, pEC50) 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. Structure-based generative modeling is rapidly reshaping drug discovery by enabling pocket-aware ligand design alongside predictive evaluation of binding properties. This manuscript introduces FLOWR.root, an SE(3)-equivariant flow-matching framework that jointly generates high-quality 3D ligands and predicts multi-endpoint binding affinities, demonstrating state-of-the-art performance, efficient domain adaptation, and practical impact across de novo design, scaffold elaboration, and lead optimization workflows.
Julian Cremer, Tuan Le, M. Ghahremanpour et al.· Nature Communications· 1 citation
We present Flowr.root, an SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation with joint binding affinity prediction and confidence estimation. The model supports multiple design modes including de novo generation, interaction/pharmacophore-conditional sampling, fragment elaboration, and multi-endpoint affinity prediction (pIC50, pKi, pKd, pEC50). Training combines large-scale ligand libraries with mixed-fidelity protein–ligand complexes, followed by refinement on curated co-crystal datasets and adaptation to project-specific data through parameter-efficient finetuning. Flowr.root achieves state-of-the-art performance in both unconditional 3D molecule and pocket-conditional ligand generation, producing geometrically realistic, low-strain structures with computational efficiency on established benchmark datasets. The integrated affinity prediction module demonstrates superior accuracy on the Spindr test set and outperforms recent models on the Schrödinger FEP+/OpenFE benchmark while offering substantial speed advantages. As a foundation model, Flowr.root requires continuous parameter-efficient finetuning on project-specific datasets to account for unseen structure-activity landscapes, which we demonstrate yields strong correlation with experimental in-house data. The model’s joint generation and affinity prediction capabilities enable inference-time scaling through importance sampling, effectively steering molecular design toward higher-affinity compounds. Case studies validate this approach: selective CK2α ligand generation against CLK3 shows significant correlation between predicted and quantum-mechanical binding energies, while scaffold elaboration studies on ERα, TYK2 and BACE1 demonstrate strong agreement between predicted affinities and QM calculations. By integrating structure-aware generation, affinity estimation, and property-guided sampling within a unified framework, Flowr.root provides a comprehensive foundation for structure-based drug design spanning hit identification through lead optimization.
Julian Cremer, Tuan Le, M. Ghahremanpour et al.· arXiv.org· 5 citations· ⚡1