Identification of structural fragments and field point-based design of novel p38α MAPK inhibitor: Integrating 2D and 3D-QSAR models with advanced in-silico techniques.

Dysregulation of p38α MAP kinase (MAPK14) increases the production of pro-inflammatory cytokines, causing pathogenesis of inflammatory, oncological, and neurodegenerative diseases. Till date, no orally effective p38α MAPK inhibitor exists in clinics, which may be due to poor selectivity and off-target effects. This emphasizes the urgent need for the design of potential p38α MAPK inhibitors. In this study, SMILES-based 2D-QSAR and field-point-based 3D QSAR models were developed to guide the design of novel p38α MAPK inhibitors. A dataset of 207 molecules was used to developed 2D-QSAR models via Monte Carlo optimization. Among fifteen models, Split-3 of model 14 exhibited highest statistical performance and was identified as the best model. Structural fragments that either enhance or hinder activity were identified. Subsequently for 3D-QSAR approach, a pharmacophoric template was generated and employed to align dataset. This aligned dataset was utilized to developed 3D-QSAR and a 5-component model showed superior predictivity and provided SAR insights. Based on these insights, a virtual library of 14,040 compounds was designed and screened using in silico workflow such as Lipinski's Rule of Five, predicted pIC50, molecular docking, electrostatic complementarity, molecular dynamics simulations, MM/GBSA, WaterSwap, and ADMET predictions. From this virtual screening, compound P38S002073 emerged as the most promising lead candidate. Overall, this integrative approach provides important structural insights, field-based insights, and SAR for the development of potent and orally bioavailable p38α MAPK inhibitors.