Kalasin Surachate, Surareungchai Werasak
Faculty of Science and Nanoscience & Nanotechnology Graduate Program, King Mongkut's University of Technology Thonburi 10140 Thailand
Pilot Plant Research and Development Laboratory, King Mongkut's University of Technology Thonburi 10150 Bangkok Thailand.
RSC Adv. 2024 Aug 27;14(37):26897-26910. doi: 10.1039/d4ra03965c. eCollection 2024 Aug 22.
While each massive pandemic has claimed the lives of millions of vulnerable populations over the centuries, one limitation exists: that the Edisonian approach (human-directed with trial errors) relies on repurposing pharmaceuticals, designing drugs, and herbal remedies with the violation of Lipinski's rule of five druglikeness. It may lead to adverse health effects with long-term health multimorbidity. Nevertheless, declining birth rates and aging populations will likely cause a shift in society due to a shortage of a scientific workforce to defend against the next pandemic incursion. The challenge of combating the ongoing post-COVID-19 pandemic has been exacerbated by the lack of gold standard drugs to deactivate multiple SARS-CoV-2 protein targets. Meanwhile, there are three FDA-approved antivirals, Remdesivir, Molnupiravir, and Paxlovid, with moderate clinical efficacy and drug resistance. There is a pressing need for additional antivirals and prepared omics technology to combat the current and future devastating coronavirus pandemics. While there is a limitation of existing contemporary inhibitors to deactivate viral RNA replication with minimal rotational bonds, one strategy is to create Lipinski inhibitors with less than 10 rotational bonds and precise halogen bond placement to destabilize multiple viral protomers. This work describes the efforts to design gold-standard oral inhibitors of bi- and tri-cyclic catalytic interceptors with electrophilic heads using double-shell deep learning. Here, KS1 with and KS2 compounds designed by lab-on-a-chip technology attain 5-fold novel filtered-Lipinski, GHOSE, VEBER, EGAN, and MUEGGE druglikeness. The graph neural network (GNN) relies on module-initiation, expansion, relabeling atom index, and termination (METORITE) iterations, while the deep neural network (DNN) engages pinning, extraction, convolution, pooling, and flattening (PROOF) operations. The cyclic compound's specific halogen atom location enhances the nitrile catalytic head, which deactivates several viral protein targets. Initiating this lab-on-a-chip that is not susceptible to the aging process for creating clinical compounds can leverage a new path to many valuable drugs with speedy oral drug discovery, especially to defend the loss of vulnerable population and prevent multimorbidity that is susceptible to hidden viral persistence in the continuing aging times.
在过去几个世纪里,每一次大规模疫情都夺走了数百万弱势群体的生命,但存在一个局限性:爱迪生式方法(人为指导并通过试错)依赖于重新利用药物、设计药物以及草药疗法,而这违反了利平斯基的五规则药物相似性原则。这可能会导致长期健康合并症等不良健康影响。然而,出生率下降和人口老龄化可能会因缺乏抵御下一次疫情入侵的科学劳动力而导致社会发生转变。由于缺乏使多种新冠病毒蛋白靶点失活的金标准药物,应对当前新冠疫情后的挑战变得更加严峻。与此同时,美国食品药品监督管理局(FDA)批准了三种抗病毒药物,瑞德西韦、莫努匹拉韦和帕罗韦德,但它们的临床疗效中等且存在耐药性。迫切需要更多的抗病毒药物和完善的组学技术来对抗当前和未来具有毁灭性的冠状病毒疫情。虽然现有的当代抑制剂在使病毒RNA复制失活且旋转键最少方面存在局限性,但一种策略是创建旋转键少于10个且卤键位置精确的利平斯基抑制剂,以使多个病毒原聚体不稳定。这项工作描述了利用双壳深度学习设计具有亲电头部的双环和三环催化拦截器的金标准口服抑制剂的努力。在这里,通过芯片实验室技术设计的KS1和KS2化合物达到了5倍的新型过滤后的利平斯基、戈斯、韦贝尔、伊根和穆格药物相似性。图神经网络(GNN)依赖于模块初始化、扩展、重新标记原子索引和终止(METORITE)迭代,而深度神经网络(DNN)则进行固定、提取、卷积、池化和平展(PROOF)操作。环状化合物特定的卤原子位置增强了腈催化头部,从而使多种病毒蛋白靶点失活。启动这种不易受老化过程影响的芯片实验室来创建临床化合物,可以为快速口服药物研发开辟一条通往许多有价值药物的新途径,特别是为了保护弱势群体的生命,防止在持续老龄化时代易受隐藏病毒持续感染影响的合并症。