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在分子对接、分子量子相似性和化学反应性指数框架内,研究抗病毒药物对多种新冠病毒药物靶点的有效性。

Study anti-viral drugs for their efficiency against multiple SARS CoV-2 drug targets within molecular docking, molecular quantum similarity, and chemical reactivity indices frameworks.

作者信息

Morales-Bayuelo Alejandro, Sánchez-Márquez Jesús, Vivas-Reyes Ricardo, Kaya Savaş

机构信息

Universidad del Sinú, Grupo GENOMA, Cartagena, Bolivar, 36987, Colombia.

Departamento de Química-Física, Universidad de Cadiz, Cádiz, Andalusia, 544578, Spain.

出版信息

F1000Res. 2024 Dec 6;13:270. doi: 10.12688/f1000research.146350.2. eCollection 2024.

Abstract

UNLABELLED

The study focused on drug discovery for COVID-19, emphasizing the challenges posed by the pandemic and the importance of understanding the virus's biology. The research utilized molecular docking and quantum similarity analyses to explore potential ligands for SARS-CoV-2 RNA-dependent RNA polymerase.

DOCKING RESULTS

Docking outcomes for various ligands, including Oseltamivir, Prochloraz, Valacyclovir, Baricitinib, Molnupiravir, Penciclovir, Famciclovir, Lamivudine, and Nitazoxanide, were presented. Interactions between ligands and specific residues in the RNA-dependent RNA polymerase were analyzed.

REACTIVITY DESCRIPTORS

Global parameters, such as electronic chemical potential, chemical hardness, global softness, and global electrophilicity, were computed for the ligands. For the local reactivity descriptors, the Fukui Functions were used. Fukui functions, representing electrophilic and nucleophilic sites, were calculated for selected ligands (Valacyclovir and Penciclovir). Nucleophilic character assignments for specific molecular regions were discussed, providing insights into potential charge-donating interactions.

RESULTS AND DISCUSSION

Challenges in COVID-19 drug discovery, such as virus mutability, rapid evolution, and resource limitations, were summarized. Progress in vaccine development and the need for ongoing research to address variants and breakthrough cases were emphasized.

OVERLAP OPERATOR ANALYSIS

Higher MQSM between Lamivudine and Molnupiravir (0.5742) indicates structural and electronic similarity. Lowest MQSM between Oseltamivir and Prochloraz (0.2233) implies structural dissimilarity.

COULOMB OPERATOR ANALYSIS

Higher MQSM between Lamivudine and Molnupiravir (0.9178) suggests both structural and electronic similarity. Lowest MQSM between Baricitinib and Famciclovir (0.6001) indicates greater structural diversity. Measurements above 0.5 in Table 3 suggest electronic similarity, emphasizing the electronic aspects in molecular analysis.In this sense, it study employed a multi-faceted approach combining molecular docking, quantum similarity analyses, and chemical reactivity assessments to explore potential drug candidates for COVID-19. The findings provide valuable insights into ligand interactions, reactivity patterns, and the challenges associated with drug discovery in the context of the global pandemic.

摘要

未标记

该研究聚焦于新冠病毒(COVID-19)的药物发现,强调了疫情带来的挑战以及了解病毒生物学特性的重要性。该研究利用分子对接和量子相似性分析来探索严重急性呼吸综合征冠状病毒2(SARS-CoV-2)依赖RNA的RNA聚合酶的潜在配体。

对接结果

展示了包括奥司他韦、咪鲜胺、伐昔洛韦、巴瑞替尼、莫努匹韦、喷昔洛韦、泛昔洛韦、拉米夫定和硝唑尼特等各种配体的对接结果。分析了配体与依赖RNA的RNA聚合酶中特定残基之间的相互作用。

反应性描述符

计算了配体的全局参数,如电子化学势、化学硬度、全局软度和全局亲电性。对于局部反应性描述符,使用了福井函数。计算了选定配体(伐昔洛韦和喷昔洛韦)的福井函数,其代表亲电和亲核位点。讨论了特定分子区域的亲核特性归属,为潜在的电荷给予相互作用提供了见解。

结果与讨论

总结了新冠病毒药物发现中的挑战,如病毒变异性、快速进化和资源限制。强调了疫苗开发的进展以及持续研究以应对变异株和突破性病例的必要性。

重叠算子分析

拉米夫定和莫努匹韦之间的较高分子量子相似性测量值(MQSM)(0.5742)表明结构和电子相似性。奥司他韦和咪鲜胺之间的最低MQSM(0.2233)意味着结构不相似。

库仑算子分析

拉米夫定和莫努匹韦之间的较高MQSM(0.9178)表明结构和电子相似性。巴瑞替尼和泛昔洛韦之间的最低MQSM(0.6001)表明结构多样性更大。表3中高于0.5的测量值表明电子相似性,强调了分子分析中的电子方面。从这个意义上说,该研究采用了多方面的方法,结合分子对接、量子相似性分析和化学反应性评估来探索新冠病毒的潜在候选药物。这些发现为配体相互作用、反应模式以及全球疫情背景下药物发现相关的挑战提供了有价值的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1791/11916749/75be611e1863/f1000research-13-174897-g0000.jpg

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