Discovery of novel VEGFR2 inhibitors against non-small cell lung cancer based on fingerprint-enhanced graph attention convolutional network.

ABSTRACT: 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 relation

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11613592 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature