TY  - JOUR
T1  - Computational Exploration of IP6K1 Inhibition: Ligand- and Structure-Based Insights for Obesity-Related Therapeutics
A1  - Kevin O’Donnell
A1  - Rachel S. Murphy
A1  - Brian Nolan
JF  - Pharmaceutical Sciences and Drug Design
JO  - Pharm Sci Drug Des
SN  - 3062-4428
Y1  - 2023
VL  - 3
IS  - 1
DO  - 10.51847/xYqaC4o5uP
SP  - 329
EP  - 346
N2  - Current investigations have revealed an encouraging method for managing the increasing worldwide challenge of obesity and its comorbid conditions. Targeting inositol hexakisphosphate kinase 1 (IP6K1) through inhibition has surfaced as a viable treatment avenue. The present work applies diverse ligand-based computational modeling approaches to examine the key structural elements needed for benzisoxazole compounds to inhibit IP6K1 effectively. Initially, we generated linear 2D Quantitative Structure–Activity Relationship (2D-QSAR) models to balance explanatory clarity with robust forecasting capability. Subsequently, pharmacophore modeling from ligands was carried out to detect the critical chemical features driving the high potency of these molecules. To elucidate the three-dimensional aspects required for greater efficacy toward the IP6K1 target, various alignment strategies were used to build 3D-QSAR models. Because no experimental X-ray structure exists for IP6K1, a dependable homology model was constructed and thoroughly verified structurally, allowing structure-based studies on the chosen compound set. In addition, molecular dynamics simulations employing the docked configurations of these molecules yielded deeper understanding. The outcomes uniformly reinforced the explanatory insights gained from ligand-based as well as structure-based methods. This research supplies practical recommendations for developing new IP6K1 inhibitors. Significantly, all analyses were conducted using only freely available, non-proprietary software, facilitating easy replication of the described models.
UR  - https://galaxypub.co/article/computational-exploration-of-ip6k1-inhibition-ligand-and-structure-based-insights-for-obesity-rela-klx1ycbvmwblvs0
ER  -