University of Tunis, The National Higher School of Engineering of Tunis (ENSIT), Laboratory of Signal Image and Energy Mastery, LR13ES03 (SIME), Tunis, Tunisia.
Biomed Res Int. 2021 May 25;2021:6696012. doi: 10.1155/2021/6696012. eCollection 2021.
A global pandemic has emerged following the appearance of the new severe acute respiratory virus whose official name is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strongly affecting the health sector as well as the world economy. Indeed, following the emergence of this new virus, despite the existence of a few approved and known effective vaccines at the time of writing this original study, a sense of urgency has emerged worldwide to discover new technical tools and new drugs as soon as possible. In this context, many studies and researches are currently underway to develop new tools and therapies against SARS CoV-2 and other viruses, using different approaches. The 3-chymotrypsin (3CL) protease, which is directly involved in the cotranslational and posttranslational modifications of viral polyproteins essential for the existence and replication of the virus in the host, is one of the coronavirus target proteins that has been the subject of these extensive studies. Currently, the majority of these studies are aimed at repurposing already known and clinically approved drugs against this new virus, but this approach is not really successful. Recently, different studies have successfully demonstrated the effectiveness of artificial intelligence-based techniques to understand existing chemical spaces and generate new small molecules that are both effective and efficient. In this framework and for our study, we combined a generative recurrent neural network model with transfer learning methods and active learning-based algorithms to design novel small molecules capable of effectively inhibiting the 3CL protease in human cells. We then analyze these small molecules to find the correct binding site that matches the structure of the 3CL protease of our target virus as well as other analyses performed in this study. Based on these screening results, some molecules have achieved a good binding score close to -18 kcal/mol, which we can consider as good potential candidates for further synthesis and testing against SARS-CoV-2.
一种新的严重急性呼吸病毒出现后，引发了全球大流行，该病毒的正式名称为严重急性呼吸综合征冠状病毒 2（SARS-CoV-2），它严重影响了卫生部门和世界经济。事实上，自从这种新病毒出现以来，尽管在撰写本原始研究时已经存在少数经过批准和已知有效的疫苗，但全球范围内都出现了一种紧迫感，即尽快发现新的技术工具和新药物。在这种情况下，目前正在进行许多研究和研究，以使用不同的方法开发针对 SARS-CoV-2 和其他病毒的新工具和疗法。3-糜蛋白酶（3CL）蛋白酶直接参与病毒多蛋白的共翻译和翻译后修饰，这些修饰对于病毒在宿主中的存在和复制至关重要，是冠状病毒靶蛋白之一，一直是这些广泛研究的主题。目前，这些研究大多旨在重新利用已经针对这种新病毒获得批准和临床批准的药物，但这种方法并不是很成功。最近，不同的研究成功地证明了基于人工智能的技术可以有效地理解现有化学空间并生成新的小分子，这些小分子既有效又高效。在这个框架内，为了我们的研究，我们结合了生成式递归神经网络模型与迁移学习方法和基于主动学习的算法，设计了能够有效抑制人类细胞中 3CL 蛋白酶的新型小分子。然后，我们对这些小分子进行分析，以找到与我们目标病毒的 3CL 蛋白酶结构相匹配的正确结合位点，以及本研究中进行的其他分析。基于这些筛选结果，一些分子达到了接近-18kcal/mol 的良好结合评分，我们可以将其视为进一步合成和针对 SARS-CoV-2 测试的良好候选物。
