Aqeel Imra, Majid Abdul, Albanyan Abdullah, Wasfi Hassan
Biomedical Information Research Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad, 45650, Pakistan.
College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Sci Rep. 2025 May 28;15(1):18722. doi: 10.1038/s41598-025-02773-7.
The COVID-19 pandemic has initiated a global health emergency, with an exigent need for an effective cure. Progressively, drug repurposing is emerging as a promising solution for saving time, cost, and labor. However, the number of drug candidates that have been identified for the treatment of COVID-19 is still insufficient, so more effective and thorough drug exploration strategies are required. In this study, we joined the molecular docking with machine learning approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the replicating enzyme 3CLpro of SARS-CoV-2. Molecular docking is used to calculate the binding affinities of these drugs towards 3CLpro. We employed several machine learning approaches for QSAR modeling to explore some potential drugs with high binding affinities. Our outcomes demonstrated that the Decision Tree Regression (DTR) model, with the best scores of R² and RMSE, is the most suitable model to explore the potential drugs. We shortlisted six favorable drugs with their respective Zinc IDs (3873365, 85432544, 203757351, 85536956, 8214470, and 261494640) within the range of -15 kcal/mol to -13 kcal/mol. We further examined the physiochemical and pharmacokinetic properties of these most potent drugs. Our study provides an efficient framework to explore the potential drugs against COVID-19 and establishes the impending combination of molecular docking with machine learning approaches to accelerate the identification of potential therapeutic candidates. Our verdicts contribute to the larger goal of finding effective cures for COVID-19, which is an acute global health challenge. The outcomes of our study provide valuable insights into potential therapeutic candidates for COVID-19 treatment.
新冠疫情引发了全球卫生紧急状况，迫切需要有效的治疗方法。逐渐地，药物重新利用正成为节省时间、成本和人力的一种有前景的解决方案。然而，已确定用于治疗新冠的候选药物数量仍然不足，因此需要更有效、更全面的药物探索策略。在本研究中，我们将分子对接与机器学习方法相结合，以寻找一些用于治疗新冠的潜在治疗候选药物。我们针对新冠病毒复制酶3CLpro对5903种已批准药物进行了抑制筛选。分子对接用于计算这些药物与3CLpro的结合亲和力。我们采用了几种机器学习方法进行定量构效关系建模，以探索一些具有高结合亲和力的潜在药物。我们的结果表明，决定树回归（DTR）模型的R²和均方根误差（RMSE）得分最佳，是探索潜在药物的最合适模型。我们筛选出六种有利药物，其各自的锌离子编号（3873365、85432544、203757351、85536956、8214470和261494640）在-15千卡/摩尔至-13千卡/摩尔范围内。我们进一步研究了这些最有效药物的理化和药代动力学性质。我们的研究提供了一个探索抗新冠潜在药物的有效框架，并确立了即将到来的分子对接与机器学习方法的结合，以加速潜在治疗候选药物的识别。我们的结论有助于实现为新冠找到有效治疗方法这一更大目标，新冠是一项严峻的全球卫生挑战。我们研究的结果为新冠治疗的潜在治疗候选药物提供了有价值的见解。
