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 Q² was used to describe the results. And the MIX-SVM model showed the best prediction ability and strongest model robustness: R² = 0.927, Q² = 0.916, mean square error (MSE) = 0.027 for the training set and R² = 0.946, Q² = 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。基于从描述符池中选择的五个描述符，通过启发式方法（HM）、随机森林（RF）、径向基核函数支持向量机（RBF-SVM）和混合核函数支持向量机（ MIX-SVM）包括径向基核和多项式核函数。此外，RF 建立的模型解释了 HM 选择的描述符的重要性。与 RBF-SVM 相比，MIX-SVM同时增强了所构建模型的泛化和学习能力，该方法中的多参数优化问题也通过粒子群优化（PSO）算法解决，复杂度极低，收敛速度快。此外，采用留一法交叉验证（LOO-CV）来测试模型的稳健性和Q ²用于描述结果。并且MIX-SVM模型表现出最好的预测能力和最强的模型鲁棒性：训练集的R ²  = 0.927，Q ²  = 0.916，均方误差（MSE）= 0.027，R ²  = 0.946，Q ²  = 0.913，MSE = 0.023 用于测试集。该研究揭示了 1,2,3-TPD 的五个关键描述符，并将为未来筛选出高效和新型药物提供帮助。