AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation

• URL: http://arxiv.org/abs/2506.05768v1
• Date: Fri, 06 Jun 2025 05:52:19 GMT
• Title: AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
• Authors: Wenyu Zhu, Jianhui Wang, Bowen Gao, Yinjun Jia, Haichuan Tan, Ya-Qin Zhang, Wei-Ying Ma, Yanyan Lan,
• Abstract summary: We introduce an alignment-and-aggregation framework to enable accurate virtual screening under structural uncertainty.<n>We evaluate our method on a newly curated benchmark of apo structures, where it significantly outperforms state-of-the-art methods in blind apo setting.
• Score: 18.8920680373474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Virtual screening (VS) is a critical component of modern drug discovery, yet most existing methods--whether physics-based or deep learning-based--are developed around holo protein structures with known ligand-bound pockets. Consequently, their performance degrades significantly on apo or predicted structures such as those from AlphaFold2, which are more representative of real-world early-stage drug discovery, where pocket information is often missing. In this paper, we introduce an alignment-and-aggregation framework to enable accurate virtual screening under structural uncertainty. Our method comprises two core components: (1) a tri-modal contrastive learning module that aligns representations of the ligand, the holo pocket, and cavities detected from structures, thereby enhancing robustness to pocket localization error; and (2) a cross-attention based adapter for dynamically aggregating candidate binding sites, enabling the model to learn from activity data even without precise pocket annotations. We evaluated our method on a newly curated benchmark of apo structures, where it significantly outperforms state-of-the-art methods in blind apo setting, improving the early enrichment factor (EF1%) from 11.75 to 37.19. Notably, it also maintains strong performance on holo structures. These results demonstrate the promise of our approach in advancing first-in-class drug discovery, particularly in scenarios lacking experimentally resolved protein-ligand complexes.

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