基于适应度对齐的结构建模实现AuroBind可扩展的虚拟筛选

Fitness aligned structural modeling enables scalable virtual screening with AuroBind

August 4, 2025

作者: Zhongyue Zhang, Jiahua Rao, Jie Zhong, Weiqiang Bai, Dongxue Wang, Shaobo Ning, Lifeng Qiao, Sheng Xu, Runze Ma, Will Hua, Jack Xiaoyu Chen, Odin Zhang, Wei Lu, Hanyi Feng, He Yang, Xinchao Shi, Rui Li, Wanli Ouyang, Xinzhu Ma, Jiahao Wang, Jixian Zhang, Jia Duan, Siqi Sun, Jian Zhang, Shuangjia Zheng

cs.AI

摘要

绝大多数人类蛋白质尚未被药物靶向，超过96%的人类蛋白质未被已批准的治疗药物开发利用。尽管基于结构的虚拟筛选有望扩展可药物化的蛋白质组，但现有方法缺乏原子级精度，且无法预测结合适应性，限制了其转化应用。我们提出了AuroBind，一个可扩展的虚拟筛选框架，该框架在百万级化学基因组数据上微调了定制的原子级结构模型。AuroBind整合了直接偏好优化、高置信度复合物的自蒸馏以及师生加速策略，共同预测配体结合结构和结合适应性。所提出的模型在结构和功能基准测试中超越了现有最先进模型，同时实现了在超大规模化合物库中100,000倍速的筛选。在对十个疾病相关靶点的前瞻性筛选中，AuroBind的实验命中率达到7-69%，其中顶级化合物展现出亚纳摩尔至皮摩尔级别的效力。对于孤儿GPCRs GPR151和GPR160，AuroBind成功识别了激动剂和拮抗剂，成功率在16-30%之间，功能实验证实了GPR160在肝癌和前列腺癌模型中的调节作用。AuroBind为结构功能学习和高通量分子筛选提供了一个通用框架，弥合了结构预测与治疗发现之间的鸿沟。

English

Most human proteins remain undrugged, over 96% of human proteins remain unexploited by approved therapeutics. While structure-based virtual screening promises to expand the druggable proteome, existing methods lack atomic-level precision and fail to predict binding fitness, limiting translational impact. We present AuroBind, a scalable virtual screening framework that fine-tunes a custom atomic-level structural model on million-scale chemogenomic data. AuroBind integrates direct preference optimization, self-distillation from high-confidence complexes, and a teacher-student acceleration strategy to jointly predict ligand-bound structures and binding fitness. The proposed models outperform state-of-the-art models on structural and functional benchmarks while enabling 100,000-fold faster screening across ultra-large compound libraries. In a prospective screen across ten disease-relevant targets, AuroBind achieved experimental hit rates of 7-69%, with top compounds reaching sub-nanomolar to picomolar potency. For the orphan GPCRs GPR151 and GPR160, AuroBind identified both agonists and antagonists with success rates of 16-30%, and functional assays confirmed GPR160 modulation in liver and prostate cancer models. AuroBind offers a generalizable framework for structure-function learning and high-throughput molecular screening, bridging the gap between structure prediction and therapeutic discovery.