药物-靶点相互作用（DTI）在药物重新利用和阐明药物作用机制中发挥着关键作用。虽然单靶点药物已在临床上取得成功，但它们对癌症等复杂疾病的疗效往往有限，其发展和治疗取决于多种生物过程。因此，在寻求有效且安全的癌症和其他适应症治疗方法时，全面了解主要、次要甚至非活性靶点变得至关重要。人类蛋白质组提供了一千多个可药物靶标，但大多数 FDA 批准的药物仅与这些靶标中的一小部分结合。这项研究引入了一种基于注意力的方法（称为 MMAtt-DTA）来预测人类蛋白质的药物靶标生物活性七个超家族内。我们仔细检查了九个不同的描述符集，以确定用于预测新型 DTI 的最佳签名描述符。我们的测试结果表明，七个总科中有六个的 Spearman 相关性超过 0.72 (P< 0.001)。该方法优于十四种最先进的机器学习、深度学习和基于图形的方法，并且在使用独立的生物活性数据源进行测试时，大多数目标超家族保持了相对较高的性能。我们通过计算验证了来自 ChEMBL-V33 的 185,676 个药物靶点对，这些药物靶点对在模型训练期间无法获得，对于大多数超家族而言，实现了 Spearman 相关性大于 0.57 (P<0.001) 的合理性能。这强调了所提出的预测新型 DTI 方法的稳健性。最后，我们应用我们的方法来预测 ChEMBL-V33 中 3,492 个已批准分子中缺失的生物活性，为推进药物机制发现和重新利用现有药物用于新适应症提供了宝贵的工具。https://github.com/AronSchulman/MMAtt-DTA。补充数据可在生物信息学在线获取。© 作者 2024。由牛津大学出版社出版。

Drug-target interactions (DTIs) hold a pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets, yet most FDA-approved drugs bind to only a small fraction of these targets.This study introduces an attention-based method (called as MMAtt-DTA) to predict drug-target bioactivities across human proteins within seven superfamilies. We meticulously examined nine different descriptor sets to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P < 0.001) for six out of seven superfamilies. The proposed method outperformed fourteen state-of-the-art machine learning, deep learning and graph-based methods and maintained relatively high performance for most target superfamilies when tested with independent bioactivity data sources. We computationally validated 185,676 drug-target pairs from ChEMBL-V33 that were not available during model training, achieving a reasonable performance with Spearman correlation greater than 0.57 (P < 0.001) for most superfamilies. This underscores the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing bioactivities among 3,492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications.https://github.com/AronSchulman/MMAtt-DTA.Supplementary data are available at Bioinformatics online.© The Author(s) 2024. Published by Oxford University Press.