Wang Zixiao, Sun Lili, Xu Yu, Huang Jing, Yang Fang, Chang Yu
Department of Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, China.
Department of Pharmacy, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
J Transl Med. 2024 Dec 3;22(1):1097. doi: 10.1186/s12967-024-05893-2.
Despite the proven inhibitory effects of drugs targeting vascular endothelial growth factor receptor 2 (VEGFR2) on solid tumors, including non-small cell lung cancer (NSCLC), the development of anti-NSCLC drugs solely targeting VEGFR2 still faces risks such as off-target effects and limited efficacy. This study aims to develop a novel fingerprint-enhanced graph attention convolutional network (FnGATGCN) model for predicting the activity of anti-NSCLC drugs. Employing a multimodal fusion strategy, the model integrates a feature extraction layer that comprises molecular graph feature extraction and molecular fingerprint feature extraction. The performance evaluation results indicate that the model exhibits high accuracy and stability in predicting activity. Moreover, we explored the relationship between molecular features and biological activity through visualization analysis, thus improving the interpretability of the approach. Utilizing this model, we screened the ZINC database and conducted high-precision molecular docking, leading to the identification of 11 potential active molecules. Subsequently, molecular dynamics simulations and free energy calculations were performed. The results demonstrate that all 11 aforementioned molecules can stably bind to VEGFR2 under dynamic conditions. Among the short-listed compounds, the top six exhibited satisfactory inhibitory activity against VEGFR2 and A549 cells. Especially, compound Z-3 displayed VEGFR2 inhibitory with IC values of 0.88 μM, and anti-proliferative activity against A549 cells with IC values of 4.23 ± 0.45 μM. This approach combines the advantages of target-based and phenotype-based screening, facilitating the rapid and efficient identification of candidate compounds with dual activity against VEGFR2 and A549 cell lines. It provides new insights and methods for the development of anti-NSCLC drugs. Furthermore, further biological activity tests revealed that Z1-Z3 and Z6 manifested relatively strong antiproliferative activities against NCI-H23 and NCI-H460, and relatively low toxicity towards GES-1. The hit compounds were promising candidates for the further development of novel VEGFR2 inhibitors against NSCLC.
尽管靶向血管内皮生长因子受体2（VEGFR2）的药物对实体瘤（包括非小细胞肺癌（NSCLC））具有已证实的抑制作用，但仅靶向VEGFR2的抗NSCLC药物的开发仍面临诸如脱靶效应和疗效有限等风险。本研究旨在开发一种新型指纹增强图注意力卷积网络（FnGATGCN）模型，用于预测抗NSCLC药物的活性。该模型采用多模态融合策略，集成了一个特征提取层，该层包括分子图特征提取和分子指纹特征提取。性能评估结果表明，该模型在预测活性方面表现出高精度和稳定性。此外，我们通过可视化分析探索了分子特征与生物活性之间的关系，从而提高了该方法的可解释性。利用该模型，我们筛选了ZINC数据库并进行了高精度分子对接，从而鉴定出11种潜在的活性分子。随后，进行了分子动力学模拟和自由能计算。结果表明，上述11种分子在动态条件下均可与VEGFR2稳定结合。在入围的化合物中，前六种对VEGFR2和A549细胞表现出令人满意的抑制活性。特别是，化合物Z-3对VEGFR2的抑制IC值为0.88 μM，对A549细胞的抗增殖活性IC值为4.23±0.45 μM。这种方法结合了基于靶点和基于表型筛选的优点，有助于快速有效地鉴定对VEGFR2和A549细胞系具有双重活性的候选化合物。它为抗NSCLC药物的开发提供了新的见解和方法。此外，进一步的生物活性测试表明，Z1-Z3和Z6对NCI-H23和NCI-H460表现出相对较强的抗增殖活性，对GES-1的毒性相对较低。这些命中化合物是进一步开发新型抗NSCLC的VEGFR2抑制剂的有前途的候选物。
