Identification of novel NLRP3 inhibitors: a comprehensive approach using 2D-QSAR, molecular docking, molecular dynamics simulation and drug-likeness evaluation

Abstract

This research, employing computational methodologies, aimed to discover potential inhibitors for the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3), an intracellular sensor pivotal in in ammation and various disease processes. Despite NLRP3's critical role, there remains a research gap in the identi cation of novel inhibitors, making this study's objective signi cant. Through statistical techniques such as principal component analysis (PCA) and K-means clustering, data re nement and division was conducted in this research, leading to a more targeted set of potential inhibitors. By employing stepwise and subset multiple linear regression, a two-dimensional quantitative structureactivity relationship (2D-QSAR) model was developed, revealing six essential molecular descriptors for inhibitory activity. The interpretation of these descriptors led to the proposition of ve potential compounds. One of these proposed compounds demonstrated remarkable binding a nity through molecular docking studies, marking it as a promising inhibitor of NLRP3. Further veri cation of this compound's potential was conducted via molecular dynamics simulations, a rming its stability and interactions within the protein-ligand system. Compliance with lipinski's rule of ve indicated the drug-like properties of the proposed compounds and their potential for oral bioavailability. Consequently, these ndings present a comprehensive methodology for the discovery and evaluation of novel NLRP3 inhibitors, signi cantly contributing to potential therapeutic advancements.

Integrative Approach for Designing Novel Triazole Derivatives as α-Glucosidase Inhibitors: QSAR, Molecular Docking, ADMET, and Molecular Dynamics Investigations

“…These compounds exhibited moderate-to-high activity against α-glucosidase, as listed in Table 8, where their IC 50 values were transformed into pIC 50 values (-logIC 50 ) for QSAR modeling. The structures of the studied compounds were generated and optimized under the MMFF94 force field by using Chem3D V. 19.0.0.22 [22]. A database containing 33 substituted 1,2,4-triazoles was retrieved from a previous study [14].…”

“…Averaged and centered Moreau-Broto autocorrelation of lag 8 weighted by intrinsic state [17] VE3_Dzs Logarithmic coefficient sum of the last eigenvector from Barysz matrix weighted by Sanderson EN [18] nHsOH The count of a specific atom-type Hydrogen (H) E-State associated with hydroxy groups (OH) [19] CIC1 1-ordered complementary information content [20] RotBFrac Fraction of rotatable bonds, excluding terminal bonds [21] Pharmaceuticals 2024, 17 The results of the Y-randomization test showed that none of the random trials could match the original model, as indicated in Table 1. The lesser values for R 2 and Q 2 on each iteration, and their averages (R 2 YS = 0.195 and Q 2 YS = −0.341) suggest that the developed QSAR models are not based on random correlations.…”