Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland.
Department of Computer Science, Aalto University, Espoo, 02150, Finland.
Bioinformatics. 2024 Aug 2;40(8). doi: 10.1093/bioinformatics/btae496.
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 >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 3492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications.
药物-靶标相互作用（DTIs）在药物重定位和阐明药物作用机制方面起着关键作用。虽然单靶标药物已在临床上取得成功，但它们在治疗复杂疾病（如癌症）方面往往效果有限，因为癌症的发展和治疗依赖于多个生物学过程。因此，全面了解主要、次要甚至非活性靶标对于寻找癌症和其他适应症的有效和安全治疗方法变得至关重要。人类蛋白质组提供了超过一千个可成药的靶标，但大多数 FDA 批准的药物仅与这些靶标中的一小部分结合。
本研究介绍了一种基于注意力的方法（称为 MMAtt-DTA），用于预测七个超家族中人类蛋白质中的药物-靶标生物活性。我们仔细检查了九个不同的描述符集，以确定用于预测新的 DTI 的最佳特征描述符。我们的测试结果表明，对于七个超家族中的六个，Spearman 相关性超过 0.72（P < 0.001）。与十四种最先进的机器学习、深度学习和基于图的方法相比，该方法表现出色，并且当使用独立的生物活性数据源进行测试时，对于大多数靶标超家族保持相对较高的性能。我们计算验证了来自 ChEMBL-V33 的 185676 个药物-靶标对，这些靶标在模型训练期间不可用，对于大多数超家族，Spearman 相关性 >0.57（P < 0.001），实现了合理的性能。这突出了该方法预测新的 DTI 的稳健性。最后，我们将我们的方法应用于预测 ChEMBL-V33 中 3492 种已批准分子中的缺失生物活性，为推进药物机制发现和将现有药物重新用于新适应症提供了有价值的工具。
