基于流匹配的3D药物分子生成与亲和力预测模型，支持多任务联合优化。

We present FLOWR.root, an SE(3)-equivariant flow-matching model for pocket-aware 3D ligand generation with joint potency and binding affinity prediction and confidence estimation. The model supports de novo generation, interaction- and pharmacophore-conditional sampling, fragment elaboration and replacement, and multi-endpoint affinity prediction (pIC50, pKi, pKd, pEC50). Training combines large-scale ligand libraries with mixed-fidelity protein-ligand complexes, refined on curated co-crystal datasets and adapted to project-specific data through parameter-efficient finetuning. The base FLOWR.root model achieves state-of-the-art performance in unconditional 3D molecule and pocket-conditional ligand generation. On HiQBind, the pre-trained and finetuned model demonstrates highly accurate affinity predictions, and outperforms recent state-of-the-art methods such as Boltz-2 on the FEP+/OpenFE benchmark with substantial speed advantages. However, we show that addressing unseen structure-activity landscapes requires domain adaptation; parameter-efficient LoRA finetuning yields marked improvements on diverse proprietary datasets and PDE10A. Joint generation and affinity prediction enable inference-time scaling through importance sampling, steering design toward higher-affinity compounds. Case studies validate this: selective CK2$α$ ligand generation against CLK3 shows significant correlation between predicted and quantum-mechanical binding energies. Scaffold elaboration on ER$α$, TYK2, and BACE1 demonstrates strong agreement between predicted affinities and QM calculations while confirming geometric fidelity. By integrating structure-aware generation, affinity estimation, property-guided sampling, and efficient domain adaptation, FLOWR.root provides a comprehensive foundation for structure-based drug design from hit identification through lead optimization.