Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

可成药口袋是能够结合有机小分子的蛋白质区域，其表征对于基于靶标的药物发现至关重要。然而，导出口袋描述符具有挑战性，并且现有策略的适用性通常受到限制。我们介绍 PocketVec，一种通过类先导分子的反向虚拟筛选生成口袋描述符的方法。 PocketVec 的性能与领先的方法相当，同时解决了关键限制。此外，我们使用实验确定的结构和 AlphaFold2 模型系统地搜索人类蛋白质组中的可药物口袋，识别出 20,000 个蛋白质结构域中的 32,000 多个结合位点。然后，我们为每个站点生成 PocketVec 描述符，并进行广泛的相似性搜索，探索超过 12 亿次成对比较。我们的结果揭示了基于结构或基于序列的方法未检测到的可成药口袋的相似性，揭示了缺乏结晶抑制剂的蛋白质中相似口袋的簇，并为优先开发化学探针以探索可成药空间的策略打开了大门。