In silico insights into design of novel VEGFR-2 inhibitors: SMILES-based QSAR modelling, and docking studies on substituted benzo-fused heteronuclear derivatives

数量结构-活动关系 生物信息学 索拉非尼 对接(动物) 异核分子 化学 计算生物学 立体化学 计算机科学 计算化学 生物系统 组合化学 生物 核磁共振波谱 生物化学 基因 护理部 医学 癌症研究 肝细胞癌
作者
Satish C. Gupta,Mrinal Kashyap,Yogita Bansal,Gulshan Bansal
出处
期刊:Sar and Qsar in Environmental Research [Taylor & Francis]
卷期号:: 1-20
标识
DOI:10.1080/1062936x.2024.2332203
摘要

Eight QSAR models (M1–M8) were developed from a dataset of 118 benzo-fused heteronuclear derivatives targeting VEGFR-2 by Monte Carlo optimization method of CORALSEA 2023 software. Models were generated with hybrid optimal descriptors using both SMILES and Graphs with zero- and first-order Morgan extended connectivity index from a training set of 103 derivatives. All statistical parameters for model validation were within the prescribed limits, establishing the models to be robust and of excellent quality. Among all models, split-2 of M5 was the best-fit as reflected by rvalidation2, Qvalidation2 and MAE. Mechanistic interpretation of this model assisted the identification of structural descriptors as promoters and hinderers for VEGFR-2 inhibition. These descriptors were utilized to design novel VEGFR-2 inhibitors (YS01-YS07) by bringing modifications in compound MS90 in the dataset. Docking of all designed compounds, MS90 and sorafenib with VEGFR-2 binding site revealed favourable binding interactions. Docking score of YS07 was higher than that of MS90 and sorafenib. Molecular dynamics simulation study revealed sustained interactions of YS07 with key amino acids of VEGFR-2 at a run time of 100 ns. This study concludes the development of a best fit QSAR model which can assist the design of new anticancer agents targeting VEGFR-2.
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