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

Abstract

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.

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

“…The rationale and methodology of the Box–Jenkins based mt-QSAR modelling approach is described in the Materials and Methods section. Briefly, one of the most important factors considered for such multi-target models is their experimental elements, which are required to be decided before development of the model [27,28]. Two experimental elements, namely bt (biological target) and me (measure of effectiveness) are considered in the present analysis, depending on the nature of the current dataset.…”

“…Although the calculated total quadratic indices characterise the chemical structures of the compounds, these descriptors fail to incorporate the influence of the multiple experimental conditions on chemical structure. This problem may be sorted out by the Box–Jenkins moving average approach, which has been largely discussed previously in detail [27,28,35,38,47]. Briefly, in Box–Jenkins based mt-QSAR modelling, the calculated descriptors (or D i ) are modified to obtain deviation descriptors (∆( D i ) cj ), which represent the structural attributes of the compounds as well as the experimental conditions c j .…”

“…It is worth mentioning here that the mt-QSAR models were developed only with the remaining 4481 samples used as the modelling dataset. Once the models are developed with the modified descriptors, these were then used to screen the external validation set in order to estimate their true predictivity [28]. The best predictive model was selected based on the predictivity obtained for the external validation set.…”

QSPR/QSAR: State-of-Art, Weirdness, the Future

“…expected numerical data for groups of endpoints, which affect the phenomenon under consideration, e.g., therapeutic effect, inhibition, biocide potential, etc.). Nonetheless, traditional approaches serve as a basis to solve the task of building up multi-target QSARs, e.g., using multiple regression [99], partial least squares (PLS) [100], artificial neural networks (ANN) [101][102][103], and random forest [104].…”

“…Nowadays, multi-target QSAR is a part of "intensive" studies [96][97][98][99][100][101][102][103][104]. The development of criteria of the predictive potential of models (Table 3) also is a part of the "intensive" studies.…”