SARS-CoV-2变体受体结合域潜在抑制剂的新药物设计

The 2nd International Electronic Conference on Biomedicines Pub Date : 2023-04-24 DOI:10.3390/ecb2023-14371

E. R. Lopez

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引用次数: 0

摘要

在这项研究中，利用生成神经网络重新设计了新的潜在的SARS-CoV-2变体抑制剂。基于对接性能和ADMET曲线，表现最好的配体是CID #526。它与野生型SARS-CoV-2形成几个氢键，表明它有可能成为受体结合域的抑制剂。RBD的突变变体也与CID #526表现出良好的相互作用，这表明我们的最佳化合物对各种变体具有抑制作用。分子动力学分析表明配体- rbd配合物稳定。CID #526可以很容易地合成使用低成本的起始分子。总的来说，合成的配体值得进一步研究，以确定其作为治疗COVID-19的有效性和安全性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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De Novo Drug Design of Potential Inhibitors of the Receptor-Binding Domain of SARS-CoV-2 Variants

: In this study, novel potential inhibitors of SARS-CoV-2 variants were designed de novo using generative neural networks. The top-performing ligand based on docking performance and ADMET proﬁles was CID #526. It forms several hydrogen bonds with wild-type SARS-CoV-2, indicating its potential as an inhibitor of the receptor-binding domain. Mutated variants of the RBD also showed good interactions with CID #526, implying the inhibitory properties of our top-performing compound against various variants. Molecular dynamics analysis showed a stable ligand–RBD complex. CID #526 can easily be synthesized using low-cost starting molecules. Overall, the generated ligands merit further investigation to determine their efﬁcacy and safety as a treatment for COVID-19.

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The 2nd International Electronic Conference on Biomedicines

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