Ali Mohammed, Park In Ho, Kim Junebeom, Kim Gwanghee, Oh Jooyeon, You Jin Sun, Kim Jieun, Shin Jeon-Soo, Yoon Sang Sun
Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
Biomedicines. 2023 Nov 24;11(12):3134. doi: 10.3390/biomedicines11123134.
The integration of artificial intelligence (AI) into drug discovery has markedly advanced the search for effective therapeutics. In our study, we employed a comprehensive computational-experimental approach to identify potential anti-SARS-CoV-2 compounds. We developed a predictive model to assess the activities of compounds based on their structural features. This model screened a library of approximately 700,000 compounds, culminating in the selection of the top 100 candidates for experimental validation. In vitro assays on human intestinal epithelial cells (Caco-2) revealed that 19 of these compounds exhibited inhibitory activity. Notably, eight compounds demonstrated dose-dependent activity in Vero cell lines, with half-maximal effective concentration (EC50) values ranging from 1 μM to 7 μM. Furthermore, we utilized a clustering approach to pinpoint potential nucleoside analog inhibitors, leading to the discovery of two promising candidates: azathioprine and its metabolite, thioinosinic acid. Both compounds showed in vitro activity against SARS-CoV-2, with thioinosinic acid also significantly reducing viral loads in mouse lungs. These findings underscore the utility of AI in accelerating drug discovery processes.
将人工智能（AI）整合到药物研发中显著推进了有效治疗药物的寻找。在我们的研究中，我们采用了一种全面的计算-实验方法来识别潜在的抗SARS-CoV-2化合物。我们开发了一个预测模型，根据化合物的结构特征评估其活性。该模型筛选了一个约700,000种化合物的文库，最终选出了前100个候选物进行实验验证。对人肠上皮细胞（Caco-2）的体外试验表明，其中19种化合物具有抑制活性。值得注意的是，8种化合物在Vero细胞系中表现出剂量依赖性活性，半数最大有效浓度（EC50）值在1 μM至7 μM之间。此外，我们利用聚类方法确定潜在的核苷类似物抑制剂，从而发现了两种有前景的候选物：硫唑嘌呤及其代谢物硫代次黄嘌呤核苷酸。这两种化合物在体外均表现出对SARS-CoV-2的活性，硫代次黄嘌呤核苷酸还显著降低了小鼠肺部的病毒载量。这些发现强调了人工智能在加速药物研发过程中的作用。
