Virtual Screening of Different Subclasses of Lignans with Anticancer Potential and Based on Genetic Profile

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.

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Identification of Potential Inhibitors of SARS-CoV-2 Using Machine Learning, Molecular Docking and MD Simulation

Nayarisseri,

Bhrdwaj,

Khan

et al. 2024

Preprint

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The advent of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of the coronavirus disease 2019 (COVID-19) pandemic, has promoted physical and mental health worldwide. Due to the unavailability of effective antiviral drugs, there is an unmet demand for a robust therapeutic approach for the development of anti-SARS-CoV-2 drugs. Myriad investigations have recognized ACE2 as the primary receptor of SARS-CoV-2, and this amalgamation of ACE2 with the spike protein of the subsequent coronavirus is paramount for viral entry into host cells and inducing infection. Consequently, limiting or restricting the accessibility of the causal virus to ACE2 offers an alternative therapeutic approach for averting this illness. Thus, the objective of the study was to determine the highly efficacious inhibitors exhibiting an augmented affinity for ACE2 protein and asses their pharmacological efficacy using molecular docking analysis. Machine learning algorithms were employed to govern the novel compounds by taking the ACE2-inhibiting compounds, Quninaprill, Moexipril, etc, and pre-established repurposed viral compounds, Birinapant, Remdesivir, etc., as test datasets. Structural stability was further confirmed via MD simulation approach which comparatively assessed the novel machine-learning, and pre-established compounds, followed by toxicity and pharmacophore studies. The study therefore concludes that the novel machine-learning compound (PubChem ID: 23658468) can be a potent therapeutic agent for combatting SARS-CoV-2.

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Virtual Screening of Different Subclasses of Lignans with Anticancer Potential and Based on Genetic Profile

Cited by 4 publications

References 47 publications

8’,9’-Bis-nor-neolignans from Cedrela odorata and their antiangiogenic activity

8’,9’-Bis-nor-neolignans from Cedrela odorata and their antiangiogenic activity

Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Identification of Potential Inhibitors of SARS-CoV-2 Using Machine Learning, Molecular Docking and MD Simulation

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