The release and action of pro-inflammatory mediators such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β) are regulated by the enzyme p38 mitogen-activated protein (MAP) kinase signaling pathway [1]. p38α is a serine-threonine kinase that plays a vital role in the cellular responses to stress and inflammation by modulating gene expression, mRNA stability, and the production of cytokines. It is predominantly expressed in monocytes, macrophages, and neuronal cells. Aberrant activation of p38α has been implicated in the pathogenesis of numerous diseases, including chronic inflammatory disorders, neurodegenerative disorders, cardiovascular diseases, and cancers [2].

The pivotal role of p38α MAP kinase (MAPK) in mediating inflammation has emerged as a promising therapeutic target for a wide range of inflammatory and neurodegenerative disorders [[3], [4], [5]]. Yang et al., 2024, has comprehensively reviewed the progression of several key inhibitors of p38α MAPK from early discovery to clinical trials, including compounds such as doramapimod, pamapimod, losmapimod, talmapimod, and neflamapimod [6]. These agents have been investigated for indications, which include rheumatoid arthritis, chronic obstructive pulmonary disease (COPD), and various neurodegenerative conditions. Among them, neflamapimod, developed by EIP Pharma Inc., remains under active clinical evaluation and is currently in phase II trials for neurological conditions [7]. In year 2021, Madkour et al. provided valuable insights into the structural optimization of p38α MAPK inhibitors by classifying them on basis of number of rings in their core scaffolds. Their analysis highlighted that the inhibitory potency of these compounds is strongly influenced by the substitution pattern and orientation of the functional group, rather than the scaffold alone. Notably, Skepinone-L has been reported as the most selective p38α MAPK inhibitor reported in the literature. Although extensive research efforts are being undertaken in this area, yet no single scaffold has consistently demonstrated superior potency. Most candidates/scaffolds have failed to progress to completion of advanced-stage clinical trials due to poor isoform selectivity, low therapeutic efficacy, dose-dependent toxicity, and unfavorable pharmacokinetic profiles [8,9]. These challenges highlight the need for continued exploration of novel scaffolds and functional group optimization for improved potency, selectivity, and drug-likeness.

These research gaps and clinical limitations indicate that novel and innovative approaches are needed in the discovery and development of effective p38α MAPK inhibitors. Advanced computational approaches, such as QSAR modeling and field-based 3D alignment techniques, offer powerful tools to identify molecular features associated with biological activity and to guide the rational design of new compounds.

Few QSAR studies have been carried out on p38α MAPK inhibitors utilizing different algorithms. These studies mainly relied on small datasets and had limited chemical diversity (Table 1), which limits their utility to identify essential structural fragments and features for developing potential p38α MAPK inhibitors.

To address these limitations, the present study integrates SMILES-based 2D-QSAR and field point-based 3D-QSAR modeling with advanced in silico screening techniques for the design of novel p38α MAPK inhibitors. A chemically diverse dataset of 207 experimentally validated p38α MAPK inhibitors was used to generate statistically robust 2D-QSAR model via Monte Carlo optimization, identifying key structural fragments contributing to biological activity. Concurrently, a 3D-QSAR model based on aligned pharmacophoric features was developed to evaluate spatial field contributions. This dual-model approach facilitated the design of a 14,040-compound virtual library, subsequently screened using Lipinski's Rule of Five, predicted pIC50 through developed 2D-QSAR and 3D-QSAR models, molecular docking, electrostatic complementarity (EC) analysis, MM/GBSA single-point calculations, molecular dynamics (MD) simulations, dynamics MM-GBSA, WaterSwap binding free energy estimation, and ADMET profiling.