抗病毒类化合物结构与离解速率的关系

Relationship Between Structure and Dissociation Rates of Antiviral Compounds

为了解决如何筛选出更加有效的抗病毒类化合物的问题,研究了离解速率(k_(off))与抗病毒类药物结构的关系,理论依据是k_(off)常用于评价药物在人体开放性系统中的活性.首先,应用分子描述符软件计算出每个抗病毒类化合物的分子描述符,并使用多元逐步回归分析法、偏最小二乘法和遗传算法3种方法对描述符进行筛选.然后,分别采用支持向量机和BP神经网络方法建立抗病毒类化合物的k_(off)的预测模型,并用测试集对模型进行了验证.结果表明:筛选出了具有良好预测能力的描述符,建立的2个预测模型经验证均合理,对未来抗病毒类药物的研制具有指导意义.

To solve the problem of how to screen out more effective antiviral compounds,the relationship between dissociation rate（ koff） and antiviral drug structure was studied in this research. The theoretical basis is that koff is often used to evaluate the activity of the drug in the open system of the human body.The molecular descriptor of each antiviral compound was calculated by using molecular descriptor software,and the descriptors were screened by multiple stepwise regression analysis,partial least squares method and genetic algorithm. Then,the support vector machine（ SVM） and BP neural network were used to establish the prediction model of antiviral compound structure and dissociation rate koff value,and the model was verified. Results show that this experiment screens out the descriptors with good predictive power,and the two predictive models are proved to be reasonable and have guiding significance for the future development of antiviral drugs.