这篇论文教你用AlphaFold-3和Boltz-2做药物分子设计，DBMol能自动生成高亲和力小分子，比无条件生成覆盖更多蛋白口袋。
DBMol是一个新框架，利用AlphaFold-3和Boltz-2等结构预测模型指导小分子从头设计。它通过交替优化与投影过程：先基于Boltz-2进行梯度优化提升预测结合亲和力，再用流匹配模型将分子图映射为离散化学有效分子。实验表明DBMol能有效优化Boltz-2亲和力代理，生成分子在Boltz-2评估中具有强预测亲和力和特异性。在AF3等留出指标上，DBMol提升了口袋覆盖率并保持分子多样性，且无需参考配体监督。
DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models
Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics. Recent breakthroughs in structure prediction, such as AlphaFold-3 and Boltz-2, enable accurate biomolecular interaction prediction and show promise as foundation models for downstream tasks, including binding affinity prediction. We propose to leverage these models and introduce DBMol, a new structure predictor-guided framework for de novo small molecule design. DBMol formulates an alternating optimization and projection process. In the optimization stage, DBMol starts from an initial molecule and uses gradient-based optimization to improve pocket-specific interactions and predicted binding affinity using a structure prediction model. In the projection stage, a flow-matching model maps the optimized molecular graph to discrete and chemically valid molecules. Experiments show that DBMol effectively optimizes the Boltz-2 affinity proxy and generates molecules with strong predicted affinity and specificity under Boltz-2 evaluation. To reduce self-confirmation bias, we further evaluate generated molecules using held-out metrics, including AF3-based evaluation. DBMol substantially improves pocket coverage while maintaining molecular diversity over unconditional generation, and is competitive under held-out metrics despite the absence of reference-ligand supervision. These results support the promise of structure prediction models as effective optimization signals for de novo molecular design.