Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1)

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• MAP kinase-interacting protein kinase 1 (MNK1) is a target for cancer therapy.
• Developing potent and selective MNK1 inhibitors is crucial for therapeutic advancement.
• Quantitative structure-activity relationship (QSAR) modeling offers a computational approach to guide inhibitor design.

Purpose of the Study:

• To develop and validate robust QSAR models for predicting the inhibitory activity of MNK1 inhibitors.
• To identify key molecular descriptors influencing MNK1 inhibition.
• To utilize the best QSAR model as a screening tool for novel MNK1 inhibitor development.

Main Methods:

• Building QSAR models using simplified molecular input line entry system (SMILES) representations of 43 MNK1 inhibitors.
• Employing Monte Carlo optimization with three distinct schemes: classic, balance of correlations, and balance correlation with ideal slopes.
• Assessing model robustness using metrics such as rm(2), r(*)m(2), and a randomization technique.

Main Results:

• Successfully developed and validated QSAR models for predicting MNK1 inhibition (pIC50).
• The best QSAR model, based on single optimal descriptors, was identified.
• Investigated the structure-activity relationships of pyrazolo[1,5-a]pyrimidine derivatives and the impact of various substituents (e.g., alkyl, -OH, -NO2, halogens) on inhibitory potency.

Conclusions:

• The developed QSAR models serve as effective tools for predicting MNK1 inhibitor potency.
• The study provides insights into the structural features governing MNK1 inhibition.
• The findings facilitate the rational design and screening of novel, potent MNK1 inhibitors for therapeutic applications.