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

Abstract

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

QSAR-Co-X: An Open Source Toolkit for Multi-Target QSAR Modelling

“…Yet two additional techniques were included for establishing LDA models, namely fast-stepwise (FS-LDA) and sequential forward selection (SFS-LDA). Even though the GA implemented earlier in QSAR-Co has proved to be a highly efficient feature selection technique, judging from our previous analyses [10,19], the implementation of these feature selection techniques in QSAR-Co-X improves the scope of LDA modelling in multiple ways. Firstly, the application of more feature selection techniques enhances the chances of obtaining more predictive models especially for big data analysis [20].…”

“…At the same time, different training and validation sets 8 may be obtained by changing the random seed values. As an alternative to random data-splitting, the user may opt for a k-Means Cluster Analysis-based rational dataset division strategy (kMCA) [19,22]. In the latter option, the dataset is first divided into n (user specific) clusters on the basis of input descriptors.…”

“…Step 1-Dataset division: The first step of any mt-QSAR model encompasses a division of the initial dataset into a training and a validation set. In this module, that may be performed following three schemes, namely: (a) pre-determined data distribution, (b) random division and (c) k-means cluster analysis (kMCA) based data division [19]. In the first scheme (a), the user is allowed to explicitly provide information about the training and validation set samples, i.e.…”

QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

“…The first step of any mt-QSAR model encompasses a division of the initial dataset into a training and a validation set. In this module, that may be performed following three schemes, namely: (a) pre-determined data distribution, (b) random division and (c) k -means cluster analysis ( k MCA) based data division [ 20 ]. In the first scheme (a), the user is allowed to explicitly provide information about the training and validation set samples, i.e ., the set samples are to be tagged as ‘Train’ and ‘Test’, respectively.…”

“…As can be seen, two additional feature selection techniques were included for establishing LDA models, namely fast-stepwise (FS) and sequential forward selection (SFS). Even though the GA implemented earlier in QSAR-Co has proved to be a highly efficient feature selection technique, judging from our previous analyses [ 11 , 20 ], the implementation of these additional feature selection techniques in QSAR-Co-X improves the scope of LDA modelling in multiple ways. Firstly, the application of more feature selection techniques enhances the chances of obtaining more predictive models especially for big data analysis [ 21 ].…”