Abstract
The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure-activity relationship (QSAR) modeling, structure-activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson-Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors. The ChEMBL database was used to retrieve compounds with known IC(50) values to train machine learning (ML) models using the Lazy Predict package. After data pre-processing, 401 non-redundant structures were selected for exploratory data analysis (EDA). PubChem and MACCS fingerprints were used to predict the inhibitory activities of the compounds. SALI was used to identify structurally similar compounds with significantly different biological activities. The compounds were docked using MOE to assess their binding affinities and interactions with key residues in NLRP3. The models were evaluated, and a comparative analysis revealed that the ensemble Random Forest (RF) model (PubChem fingerprints) with RMSE (0.731), R(2) (0.622), and MAPE (8.988) and bootstrap aggregating model (MACCS fingerprints) with RMSE (0.687), R(2) (0.666), and MAPE (9.216) on the testing set performed well, in accordance with the Organization for Economic Cooperation and Development (OECD) guidelines. Out of all docked compounds, the two most promising compounds (ChEMBL5289544 and ChEMBL5219789) with binding scores of -7.5 and -8.2 kcal/mol were further investigated by MD to evaluate their stability and dynamic behavior within the binding site. MD simulations (200 ns) revealed strong structural stability, flexibility, and interactions in the selected complexes. MM/PBSA binding free energy calculations revealed that van der Waals and electrostatic forces were the key drivers of the binding of the protein with ligands. The outcomes obtained can be used to design more potent and selective NLRP3 inhibitors as therapeutic agents for the treatment of inflammatory diseases such as RA. However, concerns related to the lack of large datasets, experimental validation, and high computational costs remain.