{"ID":2886754,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.02137","arxiv_id":"2508.02137","title":"Fitness aligned structural modeling enables scalable virtual screening with AuroBind","abstract":"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.","short_abstract":"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 Aur...","url_abs":"https://arxiv.org/abs/2508.02137","url_pdf":"https://arxiv.org/pdf/2508.02137v1","authors":"[\"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\"]","published":"2025-08-04T07:34:48Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}