DataSheet1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.ZIP

PARP1 is one of six enzymes required for the highly error-prone DNA repair pathway microhomology-mediated end joining (MMEJ) and needs to be inhibited when over-expressed. In order to study the PARP1 inhibitory effect of fused tetracyclic or pentacyclic dihydrodiazepinoindolone derivatives (FTPDDs) by quantitative structure-activity relationship technique, six models were established by four kinds of methods, heuristic method, gene expression programming, random forester, and support vector regression with single, double, and triple kernel function respectively. The single, double, and triple kernel functions were RBF kernel function, the integration of RBF and polynomial kernel functions, and the integration of RBF, polynomial, and linear kernel functions respectively. The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. Among the models, the model established by support vector regression with triple kernel function, in which the optimal R2 and RMSE of training set and test set were 0.9353, 0.9348 and 0.0157, 0.0288, and R2cv of training set and test set were 0.9090 and 0.8971, shows the strongest prediction ability and robustness. The method of support vector regression with triple kernel function is a great promotion in the field of quantitative structure-activity relationship, which will contribute a lot to designing and screening new drug molecules. The information contained in the model can provide important factors that guide drug design. Based on these factors, six new FTPDDs have been designed. Using molecular docking experiments to determine the properties of new derivatives, the new drug was ultimately successfully designed.

聚腺苷二磷酸核糖聚合酶1（PARP1）是参与高出错率DNA修复通路——微同源介导末端连接（microhomology-mediated end joining, MMEJ）——的六种酶之一，当其过表达时需被抑制。为借助定量构效关系（quantitative structure-activity relationship）技术研究融合四环或五环二氢二氮杂吲哚酮衍生物（fused tetracyclic or pentacyclic dihydrodiazepinoindolone derivatives, FTPDDs）对PARP1的抑制作用，本研究采用四种方法构建了六个模型：启发式算法、基因表达式编程、随机森林，以及分别采用单核、双核与三核函数的支持向量回归。其中，单核、双核与三核函数依次为径向基核（RBF）函数、RBF核与多项式核函数的组合，以及RBF核、多项式核与线性核函数的组合。支持向量回归模型中涉及的多参数优化问题，通过粒子群优化算法得以解决。在所有构建的模型中，采用三核函数的支持向量回归所建立的模型预测能力与稳健性最优，其训练集与测试集的决定系数R²分别为0.9353、0.9348，均方根误差（root mean square error, RMSE）分别为0.0157、0.0288，训练集与测试集的交叉验证决定系数R²cv分别为0.9090与0.8971。该三核函数支持向量回归方法在定量构效关系领域取得了重要进展，可为新型药物分子的设计与筛选提供关键助力。该模型所蕴含的信息可提供指导药物设计的核心因子，基于这些因子本研究设计了六种新型FTPDDs。通过分子对接实验测定新型衍生物的结合活性与特性后，最终成功获得了候选新药分子。