背景：视网膜母细胞瘤是目前全球新生儿和儿童眼睛中最常见的恶性肿瘤，构成了危及生命的风险。化疗是视网膜母细胞瘤治疗的重要组成部分。然而，临床使用的化疗药物经常导致药物耐受性。因此，需要研究新的化疗靶向药物。VEGFR3抑制剂具有抗肿瘤生长的作用，可用于开发新的视网膜母细胞瘤靶向药物。目的：通过构建2D、3D-QSAR模型来预测药物活性、发现影响因素并设计新药物。 方法：首先利用启发式方法和基因表达编程（GEP）构建了线性和非线性QSAR模型。然后使用SYBYL软件进行比较分子相似性指数分析（COMISA）构建3D-QSAR模型。通过改变两种模型中的药物活性因素设计新药物，并进行分子对接实验。结果：使用HM创建的最佳线性模型的R2、S2和R2cv分别为0.82、0.02和0.77。对于训练集和测试集，使用GEP创建的最佳非线性模型具有相关系数分别为0.83和0.72，并且平均误差为0.02和0.04。使用SYBYL设计的3D模型通过外部验证，其高Q2（0.503）、R2（0.805）和F值（76.52），以及低的SEE值标准误差，表明该模型可靠且具有良好的预测能力。基于2D模型的分子描述符和3D模型的等高线图，以最佳活性化合物14为模板设计了100种新化合物。我们对它们进行了活性预测和分子对接实验，其中化合物14.d在组合药物活性和对接能力方面表现最佳。结论：使用GEP创建的非线性模型比使用启发式技术（HM）构建的线性模型更稳定，具有更强的预测能力。本实验设计的化合物14.d具有抗视网膜母细胞瘤的潜力，为视网膜母细胞瘤靶向药物提供了新的设计思路和方向。版权所有©2023 Ren, Gao, Li, Wang, Zhao, Wang和Liu。

Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children's eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents. Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models. Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed. Result: The best linear model created using HM had an R2, S2, and R2cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q2 (0.503), R2 (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model's reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability. Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.Copyright © 2023 Ren, Gao, Li, Wang, Zhao, Wang and Liu.