Fitness aligned structural modeling enables scalable virtual screening
  with AuroBind
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.
689225bd9dcf0e7ac9a66ec4
Zhongyue Zhang
claimed_verified
2025-08-06T19:19:12.128Z
683e5bfd53e192ceccfc8864
/avatars/723a4de85eb3ac178fd5766a796f15e2.svg
ZhongyueZhang
user
ZhongyueZhang
689225bd9dcf0e7ac9a66ec5
Jiahua Rao
689225bd9dcf0e7ac9a66ec6
Jie Zhong
689225bd9dcf0e7ac9a66ec7
Weiqiang Bai
claimed_verified
2025-08-06T19:19:17.008Z
6455c34c654d8bccae50ae45
/avatars/3b265b732614449c82d64bb6b1972de4.svg
bai
user
feimao
689225bd9dcf0e7ac9a66ec8
Dongxue Wang
689225bd9dcf0e7ac9a66ec9
Shaobo Ning
689225bd9dcf0e7ac9a66eca
Lifeng Qiao
689225bd9dcf0e7ac9a66ecb
Sheng Xu
689225bd9dcf0e7ac9a66ecc
Runze Ma
689225bd9dcf0e7ac9a66ecd
Will Hua
689225bd9dcf0e7ac9a66ece
Jack Xiaoyu Chen
689225bd9dcf0e7ac9a66ecf
Odin Zhang
689225bd9dcf0e7ac9a66ed0
Wei Lu
689225bd9dcf0e7ac9a66ed1
Hanyi Feng
689225bd9dcf0e7ac9a66ed2
He Yang
689225bd9dcf0e7ac9a66ed3
Xinchao Shi
689225bd9dcf0e7ac9a66ed4
Rui Li
689225bd9dcf0e7ac9a66ed5
Wanli Ouyang
689225bd9dcf0e7ac9a66ed6
Xinzhu Ma
689225bd9dcf0e7ac9a66ed7
Jiahao Wang
claimed_verified
2025-09-12T16:22:45.100Z
65f7f415175d2ebe6ebaf236
/avatars/48d0fc5e06df8d1f9d7e1a40a75c0af2.svg
Jeremiah.wang
user
JiaHWang
689225bd9dcf0e7ac9a66ed8
Jixian Zhang
689225bd9dcf0e7ac9a66ed9
Jia Duan
689225bd9dcf0e7ac9a66eda
Siqi Sun
689225bd9dcf0e7ac9a66edb
Jian Zhang
689225bd9dcf0e7ac9a66edc
Shuangjia Zheng
Fitness aligned structural modeling enables scalable virtual screening
  with AuroBind
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.
AuroBind is a scalable virtual screening framework that fine-tunes atomic-level structural models to predict ligand-bound structures and binding fitness, achieving high hit rates in prospective screens across disease-relevant targets.
structure-based virtual screening druggable proteome atomic-level structural model direct preference optimization self-distillation teacher-student acceleration strategy ligand-bound structures binding fitness structural benchmarks functional benchmarks high-throughput molecular screening structure-function learning therapeutic discovery