College of Computer Science and Technology, Qingdao University.
Chem Pharm Bull (Tokyo). 2022 Oct 1;70(10):684-693. doi: 10.1248/cpb.c22-00376. Epub 2022 Aug 2.
In order to predict the anti-gastric cancer effect of [1,2,3]triazolo[4,5-d]pyrimidine derivatives (1,2,3-TPD), quantitative structure-activity relationship (QSAR) studies were performed. Based on five descriptors selected from descriptors pool, four QSAR models were established by heuristic method (HM), random forest (RF), support vector machine with radial basis kernel function (RBF-SVM), and mix-kernel function support vector machine (MIX-SVM) including radial basis kernel and polynomial kernel function. Furthermore, the model built by RF explained the importance of the descriptors selected by HM. Compared with RBF-SVM, the MIX-SVM enhanced the generalization and learning ability of the constructed model simultaneously and the multi parameters optimization problem in this method was also solved by particle swarm optimization (PSO) algorithm with very low complexity and fast convergence. Besides, leave-one-out cross validation (LOO-CV) was adopted to test the robustness of the models and Q was used to describe the results. And the MIX-SVM model showed the best prediction ability and strongest model robustness: R = 0.927, Q = 0.916, mean square error (MSE) = 0.027 for the training set and R = 0.946, Q = 0.913, MSE = 0.023 for the test set. This study reveals five key descriptors of 1,2,3-TPD and will provide help to screen out efficient and novel drugs in the future.
为了预测[1,2,3]三唑并[4,5-d]嘧啶衍生物（1,2,3-TPD）的抗胃癌作用，进行了定量构效关系（QSAR）研究。基于从描述符库中选择的五个描述符，通过启发式方法（HM）、随机森林（RF）、支持向量机与径向基核函数（RBF-SVM）和混合核函数支持向量机（MIX-SVM）建立了四个 QSAR 模型，其中混合核函数包括径向基核和多项式核函数。此外，RF 模型解释了 HM 选择的描述符的重要性。与 RBF-SVM 相比，MIX-SVM 同时增强了构建模型的泛化和学习能力，并且该方法中的多参数优化问题也通过具有非常低复杂度和快速收敛的粒子群优化（PSO）算法得到解决。此外，还采用留一法交叉验证（LOO-CV）来测试模型的稳健性，并使用 Q 来描述结果。MIX-SVM 模型表现出最佳的预测能力和最强的模型稳健性：训练集的 R=0.927，Q=0.916，均方误差（MSE）=0.027，测试集的 R=0.946，Q=0.913，MSE=0.023。本研究揭示了 1,2,3-TPD 的五个关键描述符，将有助于未来筛选出高效、新颖的药物。
