Identification of potential FAK inhibitors using mol2vec molecular descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation

This study aims to identify potential focal adhesion kinase (FAK) inhibitors through an integrated computational approach, combining mol2vec descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation. A dataset of 437 compounds with known FAK inhibitory activities was used to develop QSAR models using machine learning algorithms combined with mol2vec descriptors. Subsequently, the most promising compounds were subjected to molecular docking against FAK to evaluate their binding affinities and key interactions. ADMET study and molecular dynamics simulation were also employed to investigate the pharmacokinetic, drug-like properties, and the stability of the protein-ligand complexes. The results showed that the mol2vec descriptor-based QSAR model established by support vector regression demonstrated good predictive performance (R2 = 0.813, RMSE = 0.453, MAE = 0.263 in case of training set, and R2 = 0.729, RMSE = 0.635, MAE = 0.477 in case of test set), indicating their reliability in identifying potent FAK inhibitors. Using this QSAR model and molecular docking, compound 21 (ZINC000004523722) was identified as the most potential compound, with predicted logIC50 value and binding energy of 2.59 and - 9.3 kcal/mol, respectively. The results of molecular dynamics simulation and ADMET study also further suggested its potential as a promising drug candidate. However, because our research was merely theoretical, additional in vitro and in vivo studies are required for the verification of these results.

Investigation of the mechanism of action of chemical constituents of celery seed against gout disease using network pharmacology, molecular docking, and molecular dynamics simulations

Celery (Apium graveolens L.) has long been considered as a potential herbal medicine for the prevention and treatment of gout. However, the relationship between the chemical constituents and pharmacological activities of this medicinal plant has not been fully investigated yet. Therefore, this study aims to apply network pharmacology, molecular docking and molecular dynamics to explore the relationship between the chemical constituents of celery seed and its biological effects in the treatment of gout. Network pharmacology was built and analyzed based on the data collected from GeneCards, OMIM databases and SwissTargetPrediction web server using Cytoscape 3.9.0 software. The GO and KEGG pathway analysis of the potential targets of celery seed related to gout disease was performed using the ShinyGO v0.75 app. Molecular docking and molecular dynamics were carried out using Autodock vina and NAMD 2.14 software, respectively. The network analysis identified 16 active compounds and thirteen key targets of celery seed in the treatment of gout. The GO analysis and the KEGG pathway enrichment analysis suggested that the mechanism of action of the chemical constituents of celery seed might be involved in several pathways, notably the PI3K-Akt signaling pathway, Ras signaling pathway, and HIF-1 signaling pathway, respectively. Molecular docking and molecular dynamics revealed that apiumetin might be an important chemical that plays a key role in the pharmacological effect of celery seed. These results might be useful to select the Q-markers to control the quality of the products from celery seeds.Communicated by Ramaswamy H. Sarma.