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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2508.02137 |
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| _version_ | 1866918113564426240 |
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| author | Zhang, Zhongyue Rao, Jiahua Zhong, Jie Bai, Weiqiang Wang, Dongxue Ning, Shaobo Qiao, Lifeng Xu, Sheng Ma, Runze Hua, Will Chen, Jack Xiaoyu Zhang, Odin Lu, Wei Feng, Hanyi Yang, He Shi, Xinchao Li, Rui Ouyang, Wanli Ma, Xinzhu Wang, Jiahao Zhang, Jixian Duan, Jia Sun, Siqi Zhang, Jian Zheng, Shuangjia |
| author_facet | Zhang, Zhongyue Rao, Jiahua Zhong, Jie Bai, Weiqiang Wang, Dongxue Ning, Shaobo Qiao, Lifeng Xu, Sheng Ma, Runze Hua, Will Chen, Jack Xiaoyu Zhang, Odin Lu, Wei Feng, Hanyi Yang, He Shi, Xinchao Li, Rui Ouyang, Wanli Ma, Xinzhu Wang, Jiahao Zhang, Jixian Duan, Jia Sun, Siqi Zhang, Jian Zheng, Shuangjia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02137 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fitness aligned structural modeling enables scalable virtual screening with AuroBind Zhang, Zhongyue Rao, Jiahua Zhong, Jie Bai, Weiqiang Wang, Dongxue Ning, Shaobo Qiao, Lifeng Xu, Sheng Ma, Runze Hua, Will Chen, Jack Xiaoyu Zhang, Odin Lu, Wei Feng, Hanyi Yang, He Shi, Xinchao Li, Rui Ouyang, Wanli Ma, Xinzhu Wang, Jiahao Zhang, Jixian Duan, Jia Sun, Siqi Zhang, Jian Zheng, Shuangjia Machine Learning Artificial Intelligence 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. |
| title | Fitness aligned structural modeling enables scalable virtual screening with AuroBind |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.02137 |