Pharmacophore models generation by catalyst and phase consensus-based virtual screening protocol against PI3Kα inhibitors

Discovery of 2-(5-(quinolin-6-yl)-1,3,4-oxadiazol-2-yl)acetamide derivatives as novel PI3Kα inhibitors via docking-based virtual screening

PI3Kα is an attractive target for PIK3CA mutated malignant tumor and searching for lead compounds with novel scaffold is important for the development of PI3Kα inhibitors. Therefore, the strategy of docking-based virtual screening was performed to discovery potent inhibitors. The 4L2Y_A PI3Kα crystal structure was used as the model protein receptor due to its high docking reliability. After the multistep virtual screening protocol and biological evaluation, three hits were picked up and further similarity searching led to more potent 2-(5-(quinolin-6-yl)-1,3,4-oxadiazol-2-yl)acetamide derivatives ES-25 and ES-27. In addition, the primary SAR of these novel derivatives was discussed, which provide a basis for the further structural modification.

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure–activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.

Pharmacoinformatics-based identification of chemically active molecules against Ebola virus

Ebola is a dangerous virus transmitted by animals and humans and to date there is no curable agent for such a deadly infectious disease. In this study, pharmacoinformatics-based methods were adopted to find effective novel chemical entities against Ebola virus. A well predictive and statistical robust pharmacophore model was developed from known Ebola virus inhibitors collected from the literature. The model explained the significance of each of hydrogen bond acceptor and donor, and two hydrophobic regions for activity. The National Cancer Institute and Asinex (Antiviral library) databases were screened using the final validated pharmacophore model. Initial hits were further screened with a set of criteria and finally eight molecules from both databases were proposed as promising anti Ebola agents. Further molecular docking and molecular dynamics studies were carried out and it was found that the proposed molecules possessed capability to interact with amino residues of Ebola protein as well as retaining equilibrium of protein-ligand systems. Finally, the binding energies were calculated using molecular mechanics Poisson-Boltzmann surface area approach and all proposed molecules showed strong binding affinity towards the Ebola protein receptor. Communicated by Ramaswamy H. Sarma.