School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac469.
The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an interpretable deep learning-based path-reasoning framework (iDPath) for drug discovery and repurposing by capturing MODA on by far the most comprehensive multilayer biological network consisting of the complex high-dimensional molecular interactions between genes, proteins and chemicals. Experiments show that iDPath outperforms state-of-the-art machine learning methods on a general drug repurposing task. Further investigations demonstrate that iDPath can identify explicit critical paths that are consistent with clinical evidence. To demonstrate the practical value of iDPath, we apply it to the identification of potential drugs for treating prostate cancer and hypertension. Results show that iDPath can discover new FDA-approved drugs. This research provides a novel interpretable artificial intelligence perspective on drug discovery.
药物的发现和再利用需要深入了解药物作用的机制（MODA）。现有的计算方法主要通过蛋白质-蛋白质相互作用（PPI）网络来模拟 MODA。然而，药物在人体内的分子相互作用远不止 PPI。此外，这些模型缺乏可解释性，限制了它们的实用性。我们提出了一种基于可解释深度学习的路径推理框架（iDPath），通过捕获由迄今最全面的多层生物网络（包括基因、蛋白质和化学物质之间复杂的高维分子相互作用）上的 MODA，用于药物的发现和再利用。实验表明，iDPath 在一般的药物再利用任务上优于最先进的机器学习方法。进一步的研究表明，iDPath 可以识别与临床证据一致的明确关键路径。为了展示 iDPath 的实际价值，我们将其应用于治疗前列腺癌和高血压的潜在药物的识别。结果表明，iDPath 可以发现新的 FDA 批准的药物。这项研究为药物发现提供了一种新颖的可解释的人工智能视角。
