Fitness aligned structural modeling enables scalable virtual screening with AuroBind

TL;DR

AuroBind is a scalable, atomic-level virtual screening framework that predicts ligand binding and fitness with high accuracy, enabling rapid drug discovery for previously undrugged proteins.

Contribution

It introduces a novel fine-tuning approach on chemogenomic data, integrating preference optimization and self-distillation for improved binding predictions.

Findings

Outperforms state-of-the-art models on structural and functional benchmarks.

Enables 100,000-fold faster screening of large compound libraries.

Achieves high hit rates and identifies active compounds for disease-relevant targets.

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.