A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. Resurfacing from second-rate glioma was originally classified as either mandatory or optional. Recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFRvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFR has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFR and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy. 

/pdf/structure-based-virtual-screening-molecular-docking-74nuxcgt.pdf

Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the Clinical Treatment of Glioblastoma

ABSTRACT Colorectal cancer is the third most frequent cause of cancer worldwide and is more prevalent in older individuals of all ages. The recent statistics from World Health Organisation (WHO) on cancer accounted for 1.93 million new cases of colorectal cancer alone, in 2020[1]. The exact cause of this disease is still unknown; however, obesity, sedentary lifestyle, and changed food consumption habit are thought to be driving forces. Earlier clinical studies have found non-steroidal anti-inflammatory drugs (NSAIDs) to be potent in treating colorectal cancer by inhibiting cyclooxygenase enzyme, and further research ascertained cyclooxygenase-2 gene (COX-2) inhibitors to be the most effective chemotherapy treatment. The aim of this study is to find the most effective inhibitor with a superior affinity against COX-2 protein and its pharmacological profile. The pre-established compound, parecoxib (PubChem ID: 119828) was taken up for Structure-based Virtual Screening to identify a novel compound (PubChem ID: 10151468) that has a strong binding affinity than the established compound. Additionally, the comparative studies of both the screened and established compounds were examined using the MD simulation approach to confirm structural stability. Our conclusion suggests that the virtual screened compound (PubChem ID: 10151468) could be a potential therapeutic for the treatment of colorectal cancer.

Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation and Pharmacokinetic modelling of Cyclooxygenase-2 (COX-2) inhibitor for the clinical treatment of Colorectal Cancer

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. 

pdf/potential-inhibitors-of-vegfr1-vegfr2-and-vegfr3-developed-cpk6jy4pen.pdf

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

Background Ovarian cancer (OC) is a common clinical gynecological disease, which seriously threatens women's health and life. We investigated the roles of SPOCK2 in OC and its associated molecular mechanism in the current study. Methods The expressions and prognostic value of SPOCK2 in OC were identified using the clinical data and data from the GEPIA database. Then, SPOCK2 silence was implemented to search functions of SPOCK2 in OC cells. CCK-8 was used to examine cell proliferation. Cell apoptosis was detected by flow cytometry. The OC cell invasion and migration were evaluated by transwell assays. Results Overexpressed SPOCK2 was identified in OC, which was correlated with poor prognosis and a shorter survival rate. SPOCK2 downregulation significantly suppressed OC cell proliferation, migration, and invasion, and cell apoptosis was markedly promoted by SPOCK2 silence. Meanwhile, SPOCK2 knockdown could effectively suppress Wnt/β-catenin. Conclusion SPOCK2 exerted crucial functions in OC progression and could serve as a promising candidate for OC targeted therapy.

https://downloads.hindawi.com/journals/bmri/2022/9223954.pdf

SPOCK2 Promotes the Malignant Behavior of Ovarian Cancer via Regulation of the Wnt/β-Catenin Signaling Pathway

Vascular endothelial growth factor (VEGF) and its receptor play an important role both in physiologic and pathologic angiogenesis, which is identified in ovarian cancer progression and metastasis development. The aim of the present investigation is to identify a potential vascular endothelial growth factor inhibitor which is playing a crucial role in stimulating the immunosuppressive microenvironment in tumor cells of the ovary and to examine the effectiveness of the identified inhibitor for the treatment of ovarian cancer using various in silico approaches. Twelve established VEGF inhibitors were collected from various literatures. The compound AEE788 displays great affinity towards the target protein as a result of docking study. AEE788 was further used for structure-based virtual screening in order to obtain a more structurally similar compound with high affinity. Among the 80 virtual screened compounds, CID 88265020 explicates much better affinity than the established compound AEE788. Based on molecular dynamics simulation, pharmacophore and comparative toxicity analysis of both the best established compound and the best virtual screened compound displayed a trivial variation in associated properties. The virtual screened compound CID 88265020 has a high affinity with the lowest re-rank score and holds a huge potential to inhibit the VGFR and can be implemented for prospective future investigations in ovarian cancer. 

/pdf/structure-based-virtual-screening-molecular-docking-and-2f5p47j7.pdf

Structure-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation of VEGF inhibitors for the clinical treatment of Ovarian Cancer

Potential Inhibitors of SARS-CoV-2 Developed through Machine Learning, Molecular Docking, and MD Simulation.

Structure-Based Virtual Screening, Molecular Docking, MolecularDynamics Simulation of VEGF Inhibitors for the Clinical Treatment of Ovarian Cancer

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Structure-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation of VEGF inhibitors for the clinical treatment of Ovarian Cancer

Potential Inhibitors of SARS-CoV-2 Developed through Machine Learning, Molecular Docking, and MD Simulation.

Structure-Based Virtual Screening, Molecular Docking, MolecularDynamics Simulation of VEGF Inhibitors for the Clinical Treatment of Ovarian Cancer