DrugCLIP：用于虚拟筛选的对比蛋白质-分子表示学习
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening
AI总结：
DrugCLIP将虚拟筛选视为密集检索任务，用对比学习从无标签成对数据中学习蛋白质-分子表示，在零样本基准上超越对接和监督方法且计算更快。
摘要：
虚拟筛选从庞大的化合物数据库中识别出能与特定蛋白质口袋结合的潜在药物，是AI辅助药物发现的关键步骤。传统的对接方法非常耗时，并且在实际应用中只能处理受限的搜索库。最近使用打分函数进行结合亲和力预测的监督学习方法虽有前景，但由于对带有可靠结合亲和力标签的有限数据的高度依赖，尚未超越对接方法。在本文中，我们提出了一种新颖的对比学习框架DrugCLIP，将虚拟筛选重新表述为密集检索任务，并利用对比学习从大量无显式结合亲和力分数的成对数据中对齐结合蛋白质口袋和分子的表示。我们还引入了一种受生物学知识启发的数据增强策略，以学习更好的蛋白质-分子表示。大量实验表明，DrugCLIP在多种虚拟筛选基准上显著优于传统的对接和监督学习方法，且计算时间大幅减少，尤其在零样本设置下表现突出。
英文摘要：
Virtual screening, which identifies potential drugs from vast compound databases to bind with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditional docking methods are highly time-consuming, and can only work with a restricted search library in real-life applications. Recent supervised learning approaches using scoring functions for binding-affinity prediction, although promising, have not yet surpassed docking methods due to their strong dependency on limited data with reliable binding-affinity labels. In this paper, we propose a novel contrastive learning framework, DrugCLIP, by reformulating virtual screening as a dense retrieval task and employing contrastive learning to align representations of binding protein pockets and molecules from a large quantity of pairwise data without explicit binding-affinity scores. We also introduce a biological-knowledge inspired data augmentation strategy to learn better protein-molecule representations. Extensive experiments show that DrugCLIP significantly outperforms traditional docking and supervised learning methods on diverse virtual screening benchmarks with highly reduced computation time, especially in zero-shot setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
