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

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Halder, AK (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Journal

Title: International Journal Of Molecular Sciences

Vol. 20

ISSN: 1661-6596

Publisher: MDPI

Indexing

ISI Web of Knowledge - 16 Citations

Scopus - 17 Citations

Other information

Authenticus ID: P-00R-1B7

DOI: 10.3390/ijms20174191

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17