关于设计SARS-CoVs主要蛋白酶的强效抑制剂的QSAR和分子对接研究
Fucheng Song1, Haoyang Sun2, Xiaofang Ma1
1Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China.
Frontiers in pharmacology
|June 2, 2023
概括
这项研究开发了一种可靠的QSAR模型,用于预测非对称芳香二硫化物中的SARS-CoV活性. 该模型成功地确定了治疗SARS-CoV感染的有希望的新药候选者.
科学领域:
- 药用化学 医学化学
- 计算机化药物设计技术
- 病毒学 病毒学
背景情况:
- 严重急性呼吸系统综合征冠状病毒 (SARS-CoVs) 由于死亡率高,造成了全球健康的重大风险.
- 迫切需要针对SARS-CoVs的有效抗病毒疗法.
- 这项研究的重点是开发针对SARS-CoV主要蛋白酶的新型抑制剂.
研究的目的:
- 开发一种定量结构-活性关系 (QSAR) 模型,用于预测非对称芳香二硫化物的抗病毒活性.
- 基于已识别的结构-活性关系,设计具有针对SARS-CoV主要蛋白酶增强活性的新型化合物.
- 为了指导发现强大的SARS-CoV抑制剂.
主要方法:
- 用ChemOffice构建分子结构,用CODESSA计算的描述符.
- 使用线性启发式回归方法建立QSAR模型并预测IC50值.
- 新的化合物被设计,选和分析属性,在最佳候选人身上进行分子对接.
主要成果:
- 开发了一个具有R2 = 0.87的强大的QSAR模型,证明了通过测试集验证的高统计意义和预测准确性.
- 从96种设计的衍生品中确定了5种具有有利预测IC50值的新型化合物.
- 分子对接证实了最佳化合物 (31a) 与目标蛋白质的强有力的结合能力.
结论:
- 启发式方法提供了一种可靠的方法,用于预测非对称芳二硫化物的IC50值.
- 这项研究为高活性SARS-CoV抑制剂的合理设计提供了宝贵的见解.
- 这些发现支持进一步调查这些化合物作为潜在的抗病毒药物.
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