宫颈癌是影响全球女性的普遍妇科恶性肿瘤,通常与持续的人乳头瘤病毒感染有关。与宫颈癌相关的生物标志物,包括VEGF-A,VEGF-B,VEGF-C,VEGF-D,和VEGF-E,显示上调,并与血管生成和淋巴管生成有关。本研究旨在采用计算机内方法靶向酪氨酸激酶受体蛋白VEGFR-1,VEGFR-2和VEGFR-3,并鉴定血管内皮生长因子受体(VEGFR)的新型抑制剂。进行了一项全面的文献研究,确定了26种已建立的VEGFR-1,VEGFR-2和VEGFR-3受体蛋白抑制剂。具有高亲和力得分的化合物,包括PubChemID-25102847、369976和208908是从现有化合物中选择的,用于创建基于深度学习的模型。RD-Kit,深度学习算法,用于产生针对VEGFR-1、VEGFR-2和VEGFR-3靶标的4300万个化合物。对每个靶标的前10个分子进行分子对接研究以验证受体-配体结合亲和力。分子对接的结果表明,PubChemIDs-71465,645和11152946表现出很强的亲和力,将它们指定为最有效的分子。为了进一步调查他们的潜力,进行分子动力学模拟以评估构象稳定性,并且还对灌输相互作用进行了药效分析。
Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.