Integrative Ligand-Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Simulation Approaches Identified Potential Lead Compounds against Pancreatic Cancer by Targeting FAK1

Abstract

Pancreatic cancer is a very deadly disease with a 5-year survival rate, making it one of the leading causes of cancer-related deaths globally. Focal adhesion kinase 1 (FAK1) is a ubiquitously expressed protein in pancreatic cancer. FAK, a tyrosine kinase that is overexpressed in cancer cells, is crucial for the development of tumors into malignant phenotypes. FAK functions in response to extracellular signals by triggering transmembrane receptor signaling, which enhances focal adhesion turnover, cell adhesion, cell migration, and gene expression. The ligand-based drug design approach was used to identify potential compounds against the target protein, which included molecular docking: ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized born surface area (MM-GBSA). Following the retrieval of twenty hits, four compounds were selected for further evaluation based on a molecular docking approach. Three newly discovered compounds, including PubChem CID24601203, CID1893370, and CID16355541, with binding scores of −10.4, −10.1, and −9.7 kcal/mol, respectively, may serve as lead compounds for the treatment of pancreatic cancer associated with FAK1. The ADME (absorption, distribution, metabolism, and excretion) and toxicity analyses demonstrated that the compounds were effective and nontoxic. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the cancer.

Discovery of potent heat shock protein 90 (Hsp90) inhibitors: structure-based virtual screening, molecular dynamics simulation, and biological evaluation

“…Firstly, we combined the activity compounds with bait compounds and then conducted preliminary virtual screening using the Hsp90 pharmacophore model. The ROC curve was analysed to evaluate the quality of the Hsp90 pharmacophore model 33 . As shown in Figure 3 , the AUC value was extremely close to 1.0 (0.921), indicating the good ability of the Hsp90 pharmacophore model to differentiate true actives from decoy compounds ( p < 0.001).…”

“…To validate the selectivity of the generated Hsp90 pharmacophore model, the virtual screening of a testing database including 19 known Hsp90 inhibitors and 1110 decoy compounds downloaded from the enhanced Database of Useful Decoys (DUDe) was performed using the pharmacophore search protocol of MOE 31 , 32 . Then MedCalc software was employed to analyse the receiver operating characteristic (ROC) curve 33 . In ROC analysis, the area under the curve (AUC) is generally used to assess the ability of the pharmacophore model to distinguish active or inactive compounds.…”

Exploring the Antibacterial Potential of Artemisia judaica Compounds Targeting the Hydrolase/Antibiotic Protein in Klebsiella pneumoniae: In Vitro and In Silico Investigations

“…The identification and visualization of protein binding site residues were conducted using BIOVA Discovery Studio Visualizer Tool 16.1.0, facilitating the analysis of active pockets [ 45 ]. The receptor grids for molecular docking simulation were created using binding sites acquired from a web server, utilizing PyRx tools [ 46 ].…”

Discovery of dual kinase inhibitors targeting VEGFR2 and FAK: structure-based pharmacophore modeling, virtual screening, and molecular docking studies

“…Moreover, ligandbased pharmacophore model for FAK inhibitors was used to virtually screen ZINC database identifying compound (IV) (Fig. 2) as a potential hit [57]. On the other hand, the discovery of the clinically approved VEGFR2 inhibitor, pazopanib (V) (Fig.…”