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.
Insulin resistance is a core pathological hallmark of metabolic syndromes like type 2 diabetes mellitus. Peroxisome proliferator-activated receptor gamma (PPAR-γ) is a classical therapeutic target for improving insulin sensitivity, yet the clinical utility of its synthetic agonists faces diverse adverse reactions. We previously showed that phillyrin, an important component of Forsythia suspensa, could improve insulin resistance in obesity. However, its direct molecular targets remain not fully understood. Herein, we adopted a combined in silico and experimental approach to determine whether phillyrin could function as a ligand of PPAR-γ. We implemented a multi-scale strategy integrating network pharmacology, molecular modeling, with experimental verification. Network pharmacology analysis was employed to predict system-level targets and pathways associated with phillyrin. Subsequently, molecular docking and molecular dynamics simulations were conducted using computational chemistry methods to characterize the binding mode, dynamic stability, and binding free energy (MM-GBSA) of phillyrin within the PPAR-γ ligand-binding domain. Finally, key computational predictions were experimentally validated in insulin-resistant 3T3-L1 adipocytes and in a high-fat diet-induced murine model of insulin resistance.
Ling Huang, Zhizheng Fang, Rongchun Han et al.· npj Systems Biology and Appl...· 0 citations
Inflammation involves coordinated activation of vascular, immune, lipid mediator, cytokine, transcriptional and inflammasome pathways. Modern anti-inflammatory lead discovery increasingly uses computer-aided drug design to identify plausible protein-ligand interactions before costly laboratory studies. This review summarizes the rationale for target selection, molecular docking, ADMET prediction, toxicity screening and Quality by Design-based documentation in anti-inflammatory computational pharmacology. 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. Selected natural scaffolds such as curcumin, quercetin, luteolin, apigenin, resveratrol, berberine, boswellic acid, andrographolide, withaferin A, gallic acid and ellagic acid are discussed as examples of chemically diverse candidates for pathway-based screening
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
Caspase-1 is a crucial inflammatory cysteine protease that facilitates the maturation of pro-inflammatory cytokines such as interleukin-1β and interleukin-18, making it a significant therapeutic target for inflammatory diseases. However, existing caspase-1 inhibitors often face challenges like toxicity and suboptimal drug-like properties, underscoring the need for new inhibitors. This study employed an integrated computational strategy, combining quantitative structure–activity relationship (QSAR) modeling and application of this validated model to a large natural product database followed by molecular docking, rigorous binding free energy analysis and extended molecular dynamics simulations. Initially, a dataset of 185 caspase-1 inhibitors with experimentally reported pKi values (ranging from 4.05 to 9.24) was used to construct a QSAR model using Partial Least Squares (PLS) regression. The PLS-based QSAR model was developed with 18 descriptors out of 5799 calculated descriptors for each compound and 10 latent variables, demonstrating strong statistical performance with R2 values of 0.870 and 0.838 for the training and test sets, respectively, and leave-one-out cross-validation coefficient Q2LOO and 5-fold cross-validation (Q25-fold) values of 0.819 and 0.814, respectively. Y-randomization tests further confirmed the model’s robustness, as the randomized models exhibited significantly lower statistical parameters than the original model. The validated QSAR model was applied to 276,518 natural products in the LOTUS database. Subsequent molecular docking, Molecular Mechanics/General Born Surface Area (MM/GBSA) scoring, and Pan-Assay INterference Compounds (PAINS) and Chemical Frequent Hitter (ChemFH) filtering identified 14 candidate compounds, which were further evaluated using 300 ns molecular dynamics simulations. Among these, four natural products (LTS0162325, LTS0221286, LTS0016840, and LTS0070407) showed the most stable binding behavior and maintained persistent interactions with key catalytic and substrate-binding residues of caspase-1 in a mimicked physiological condition. Overall, 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