用神经子图匹配加速药物分子筛选，大幅缩短计算时间。
PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching
- 将药效团筛选转为近似子图匹配问题，通过嵌入空间编码查询与目标关系。
- 在零样本设置下运行速度显著提升，性能与现有方法相当。
- 适合需要快速处理海量分子数据库的研究者使用。
筛选库规模的持续增长给药物发现中的虚拟筛选方法带来了重大挑战，促使我们重新审视大数据时代的传统方法。尽管3D药效团筛选仍广泛使用，但其在超大规模数据集上的应用受限于查询药效团与数据库分子匹配带来的高计算成本。本文提出PharmacoMatch，一种基于神经子图匹配的对比学习新方法。该方法将药效团筛选重新诠释为近似子图匹配问题，通过嵌入空间编码查询-目标关系，实现对构象数据库的高效查询。我们全面研究了所学表示，并在零样本设置下评估了PharmacoMatch作为预筛选工具的性能。结果表明，其运行时间显著缩短，且性能指标与现有方案相当，为大规模数据集筛选提供了有前景的加速方案。
原文摘要 · Abstract (English)
The increasing size of screening libraries poses a significant challenge for the development of virtual screening methods for drug discovery, necessitating a re-evaluation of traditional approaches in the era of big data. Although 3D pharmacophore screening remains a prevalent technique, its application to very large datasets is limited by the computational cost associated with matching query pharmacophores to database molecules. In this study, we introduce PharmacoMatch, a novel contrastive learning approach based on neural subgraph matching. Our method reinterprets pharmacophore screening as an approximate subgraph matching problem and enables efficient querying of conformational databases by encoding query-target relationships in the embedding space. We conduct comprehensive investigations of the learned representations and evaluate PharmacoMatch as pre-screening tool in a zero-shot setting. We demonstrate significantly shorter runtimes and comparable performance metrics to existing solutions, providing a promising speed-up for screening very large datasets.
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