背景：二氢蝌蚪昆酮衍生物是一类PLK1抑制剂，具有良好的抗癌活性，并有潜力作为抗胶质母细胞瘤的化疗药物。目标：本研究旨在建立2D和3D-QSAR模型，验证二氢蝌蚪昆酮衍生物的抗癌活性，并确定设计新型治疗药物的最佳结构特征。方法：使用启发式方法（HM）构建2D线性QSAR模型，利用基因表达编程（GEP）算法开发2D非线性QSAR模型。此外，引入CoMSIA方法，研究药物结构对活性的影响。设计了200个新型抗胶质瘤二氢蝌蚪昆酮化合物，并利用化学描述符和分子场图预测其活性水平。具有最高活性的化合物经过分子对接验证其结合亲和力。结果：在分析范畴内，HM线性模型的决定系数（R2）为0.6682，伴随着交叉验证决定系数（R2 cv）为0.5669和残差平方和（S2）为0.0199。GEP非线性模型在训练集和验证集上的决定系数分别为0.79和0.76。经验建模结果强调了3D-QSAR模型的卓越性，其次是GEP非线性模型，而HM线性模型表现出亚优效力。3D模型展现出出色的拟合度，具有强大的Q2（0.628）和R2（0.928）值，辅以令人印象深刻的F值（12.194）和最小化的估计标准误差（SEE）为0.160。在2D模型中，包括六个描述符，最重要的分子描述符被确定为“C-N键的最小交换能量”（MECN）。将MECN描述符与疏水场相结合，生成了用于创新药物的建议。这导致了化合物21E.153的发现，这是一种新型二氢蝌蚪昆酮衍生物，具有出色的抗肿瘤性能和对接能力。结论：2D和3D-QSAR模型的建立，以及轮廓图和分子描述符的创新整合，为胶质母细胞瘤化疗药物的设计提供了新的概念和技术。版权所有© 2023 Pan, Cheng, Wang, Lyu, Hou和Zhang。

Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma. Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents. Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity. Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2 cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as "Min exchange energy for a C-N bond" (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities. Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.Copyright © 2023 Pan, Cheng, Wang, Lyu, Hou and Zhang.