发表机构
EPFL; MRC-LMB; University of Cambridge（洛桑联邦理工学院; 医学研究委员会分子生物学实验室; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在利用结构预测模型设计高亲和力小分子。核心方法是引入DBMol框架，经交替优化和投影过程，结合结构预测模型优化分子。主要贡献是有效优化亲和力代理，提高口袋覆盖率，保持分子多样性，在无参考配体监督下有竞争力。
AI 中文摘要
设计与特定蛋白质口袋高亲和力结合的小分子配体是药物发现的基本目标，小分子构成了大部分获批疗法。结构预测的突破，如AlphaFold-3和Boltz-2，能准确预测生物分子相互作用。我们提出利用这些模型并引入DBMol，一种新的结构预测器引导的从头小分子设计框架。DBMol制定交替优化和投影过程，优化阶段从初始分子开始，用基于梯度的优化和结构预测模型改善口袋特异性相互作用及预测的结合亲和力，投影阶段通过流匹配模型将优化的分子图映射到离散且化学有效的分子。实验表明DBMol有效优化Boltz-2亲和力代理，生成具有强预测亲和力和特异性的分子。为减少自我确认偏差，还用包括基于AF3评估等保留指标进一步评估生成的分子。DBMol显著提高口袋覆盖率，保持分子多样性，在保留指标下具有竞争力。这些结果支持结构预测模型作为从头分子设计有效优化信号的前景。
英文摘要
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.
