Significance estimation for sequence-based chemical similarity searching (PhAST) and application to AuroraA kinase inhibitors

Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

Employing the combined strategy to identify novel CCR5 inhibitors and provide a basis for rational drug design.

Identification of Aurora-A Inhibitors by Ligand and Structure-Based Virtual Screening

Aurora kinase A has been identified as one of the most attractive targets for cancer therapy because of its critical role in the regulation of the cell cycle. In order to identify active compounds with structural diversity we performed virtual screening. 3D-QSAR pharmacophore models were developed and the best model was used as a query for screening the databases. Ligand and structure-based virtual screening protocol was conducted sequentially by applying the common feature pharmacophore and molecular docking to discover potent Aurora-A inhibitors. A total of eighty-eight compounds were selected for the in vitro activities against various human cancer cell lines (DU145 and HT29). Considering the activity data, we have identified seven compounds to be considered for the next step, among which four compounds had high inhibition rate (above 50 %) at 10 µM with GI50 lower than 10 µM. Based on the cell line and enzyme assay (Aurora-A & B) result, these four compounds were used as template/query molecule for similarity search. The best result was obtained for similarity hit SH3. It had IC50 of 0.578 and 11.77 µM for Aurora-A and B respectively, which implies 20-fold selectivity over Aurora-B. The hits obtained from this screening scheme could be potential drug candidates after further optimization.