Aug 2026· Molecular diversity· 0 citations· 76 references
Medicine
TL;DR
Meta-iPPAR is developed, an integrative in silico framework combining stacked machine learning, molecular docking, and molecular dynamics simulations for the identification of PPAR-γ agonists that will be an effective computational tool for screening and prioritizing potential compounds targeting PPAR-γ in the early stage of drug development pipelines.
This work adopted a combined in silico and experimental approach to determine whether phillyrin could function as a ligand of PPAR-γ, and implemented a multi-scale strategy integrating network pharmacology, molecular modeling, with experimental verification.
Ling Huang, Zhizheng Fang, Rongchun Han et al.· npj Systems Biology and Appl...· 0 citations
The article emphasizes that docking scores are hypothesis-generating outputs and must be interpreted with binding-pose quality, residue relevance, pharmacokinetic feasibility and safety prediction.
Vikas M. Mohanale, Dr. Ravi U. Kurhade, Dr. Manoj H. Dev, Amol S. Dhakpade, Nishinandan M. Shinde· International Journal of Adv...· 0 citations
An integrated GNN-guided virtual screening and core-hopping workflow tailored specifically to PPAR-γ modulator design is proposed, built around the goal of identifying selective partial agonists and biased ligands that retain insulin-sensitizing efficacy while minimizing helix-12-driven adverse transcriptional programs.
Deepansha Gandhi, Megha Mishra, Kalyani Bokde et al.· Journal of Dynamics and Cont...· 0 citations
Findings identify CP20 as a promising lead scaffold for the development of novel DPP4 inhibitors and demonstrate the effectiveness of an ensemble machine learning-guided computational framework for accelerating antidiabetic drug discovery.
Iqra Anwar, T. Chohan, Drakhshaan et al.· Journal of Computational Bio...· 0 citations
This study highlights natural diterpenoids and coumarin glycosides as promising scaffolds for caspase-1 inhibition and demonstrates that integrating QSAR modeling with structure-based approaches provides an efficient strategy for discovering potential anti-inflammatory drug candidates.
Yusuf Şeflekçi, Alper Yılmaz, Abdulilah Ece· Molecules· 0 citations
Introduction: The α-amylase enzyme plays a critical role in the digestion of complex carbohydrates. Inhibiting this enzyme offers a promising strategy for improving glucose regulation in diabetic patients. Methods: In this study, a comprehensive computational approach, combining 3D-QSAR modeling, ADMET profiling, molecular docking, molecular dynamics, ligand transport analysis, and retrosynthesis, was used to identify novel ligands with potent inhibitory activity against various indenoquinoxaline-phenylacrylohydrazide hybrids. Results: The optimal 3D-QSAR model, developed using partial least squares (PLS) and Comparative Molecular Similarity Indices Analysis (CoMSIA), demonstrated strong correlation and predictive power (Q2=0.541, R2=0.973, SEE=0.076). ADMET analysis showed that the designed ligands possess acceptable pharmacokinetic and toxicological profiles, supporting their potential for further drug development. Molecular docking revealed that the designed ligands effectively interacted with the active site of α-amylase (PDB ID: 7TAA). Furthermore, molecular dynamics simulations (100 ns) and MM-PBSA free energy calculations confirmed the stability of ligand-enzyme complexes. Ligand transport was further examined using the CaverDock program, tracking the movement of molecules from the enzyme’s active site to its surface. Finally, retrosynthetic analysis was performed to propose feasible synthesis routes for the most active compound. Conclusion: Overall, the findings highlight a promising lead compound for further in vitro and in vivo investigations targeting α-amylase inhibition.
L. Naanaai, M. Alaqarbeh, Abdellah El Aissouq et al.· BioImpacts· 0 citations
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