Fitness aligned structural modeling enables scalable virtual screening
  with AuroBind
Authors: name: Zhongyue Zhang, name: Jiahua Rao, name: Jie Zhong, name: Weiqiang Bai, name: Dongxue Wang, name: Shaobo Ning, name: Lifeng Qiao, name: Sheng Xu, name: Runze Ma, name: Will Hua, name: Jack Xiaoyu Chen, name: Odin Zhang, name: Wei Lu, name: Hanyi Feng, name: He Yang, name: Xinchao Shi, name: Rui Li, name: Wanli Ouyang, name: Xinzhu Ma, name: Jiahao Wang, name: Jixian Zhang, name: Jia Duan, name: Siqi Sun, name: Jian Zhang, name: Shuangjia Zheng

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.