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Speeding up Early Drug Discovery in Antiviral Research: A Fragment-Based in Silico Approach for the Design of Virtual Anti-Hepatitis C Leads
ACS Combinatorial Science ( IF 3.903 ) Pub Date : 2017-05-01 00:00:00 , DOI: 10.1021/acscombsci.7b00039
Alejandro Speck-Planche 1 , M. Natália Dias Soeiro Cordeiro 1
Affiliation  

Hepatitis C constitutes an unresolved global health problem. This infectious disease is caused by the hepatotropic hepatitis C virus (HCV), and it can lead to the occurrence of life-threatening medical conditions, such as cirrhosis and liver cancer. Nowadays, major clinical concerns have arisen because of the appearance of multidrug resistance (MDR) and the side effects especially associated with long-term treatments. In this work, we report the first multitasking model for quantitative structure-biological effect relationships (mtk-QSBER), focused on the simultaneous exploration of anti-HCV activity and in vitro safety profiles related to the absorption, distribution, metabolism, elimination, and toxicity (ADMET). The mtk-QSBER model was created from a data set formed by 40 158 cases, displaying accuracy higher than 95% in both training and prediction (test) sets. Several molecular fragments were selected, and their quantitative contributions to anti-HCV activity and ADMET profiles were calculated. By combining the analysis of the fragments with positive contributions and the physicochemical meanings of the different molecular descriptors in the mtk-QSBER, six new molecules were designed. These new molecules were predicted to exhibit potent anti-HCV activity and desirable in vitro ADMET properties. In addition, the designed molecules have good druglikeness according to the Lipinski’s rule of five and its variants.

中文翻译:

加快抗病毒研究中的早期药物发现:基于片段的计算机模拟方法设计虚拟抗丙型肝炎病毒线索

丙型肝炎构成了尚未解决的全球健康问题。这种传染病是由丙型肝炎病毒(HCV)引起的,可导致发生危及生命的医学疾病,例如肝硬化和肝癌。如今,由于多药耐药性(MDR)的出现以及特别是与长期治疗相关的副作用,引起了临床上的重大关注。在这项工作中,我们报告了第一个用于定量结构生物学效应关系的多任务模型(mtk-QSBER),重点在于同时探索抗HCV活性和与吸收,分布,代谢,消除和吸收有关的体外安全性毒性(ADMET)。mtk-QSBER模型是根据40158个案例组成的数据集创建的,在训练和预测(测试)集中显示的准确率均高于95%。选择了几个分子片段,并计算了它们对抗HCV活性和ADMET谱的定量贡献。通过对具有积极贡献的片段的分析以及mtk-QSBER中不同分子描述符的物理化学含义相结合,设计了六个新分子。预计这些新分子将表现出有效的抗HCV活性和理想的体外ADMET特性。此外,根据李宾斯基的五个规则及其变体,设计的分子具有良好的药物相似性。通过对具有积极贡献的片段的分析以及mtk-QSBER中不同分子描述符的物理化学含义相结合,设计了六个新分子。预计这些新分子将表现出有效的抗HCV活性和理想的体外ADMET特性。此外,根据李宾斯基的五个规则及其变体,设计的分子具有良好的药物相似性。通过对具有积极贡献的片段的分析以及mtk-QSBER中不同分子描述符的物理化学含义相结合,设计了六个新分子。预计这些新分子将表现出有效的抗HCV活性和理想的体外ADMET特性。此外,根据李宾斯基的五个规则及其变体,设计的分子具有良好的药物相似性。
更新日期:2017-05-01
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