Flowr.root – A Flow Matching Based Foundation Model for Joint Multi-Purpose Structure-Aware 3D Ligand Generation and Affinity Prediction 1. Flowr.root is a novel SE(3)-equivariant flow-matching model that revolutionizes 3D ligand generation and binding affinity prediction. It integrates pocket-aware ligand design with multi-endpoint affinity prediction, offering a unified framework for structure-based drug design. 2. The model supports multiple design modes, including de novo generation, interaction/pharmacophore-conditional sampling, fragment elaboration, and multi-endpoint affinity prediction. This versatility makes it suitable for various stages of drug discovery, from hit identification to lead optimization. 3. Flowr.root achieves state-of-the-art performance in both unconditional 3D molecule and pocket-conditional ligand generation. It produces geometrically realistic, low-strain structures with high computational efficiency, outperforming recent models on established benchmark datasets. 4. 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. 5. As a foundation model, Flowr.root requires continuous parameter-efficient fine-tuning on project-specific datasets to account for unseen structure-activity landscapes. This approach yields strong correlation with experimental in-house data, making it a dynamic companion for early-stage drug discovery campaigns. 6. 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, showing significant correlation between predicted and quantum-mechanical binding energies. 7. 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. 📜Paper: arxiv.org/abs/2510.02578 #FlowrRoot #DrugDiscovery #StructureBasedDesign #LigandGeneration #AffinityPrediction