ArticleBioinformatics and biology insights2025
Exploration of Natural Products for Targeting IDH1/2 Mutations in Acute Myeloid Leukemia Through Ligand-Based Pharmacophore Screening, Docking, ADME-T, and Molecular Dynamic Simulation Approaches.
Article in Bioinformatics and biology insights, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Computational Modeling and MM-PBSA-Based Molecular Dynamics Investigation of Unexplored tRNA Isoleucine Lysidine Synthetase from Mycobacterium tuberculosis with Hyperbranched Amoxicilloyl-Poly-L-Lysine.Journal of molecular modeling · 2026Article
- Multiscale predictive cellular modeling: integrating hypothesis grammars, digital twins, and multi-omics for In silico oncology and precision theranostics.Functional & integrative genomics · 2026Review
- Targeting IDH-driven Oncometabolism in glioblastoma: a computational approach for the identification of potential inhibitors.Frontiers in bioinformatics · 2026Article
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Authors and funding
4 authors.
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Abstract
Background: Mutations in isocitrate dehydrogenase 1 (IDH1) and 2 (IDH2) are prevalent drivers of acute myeloid leukemia (AML). While targeted therapies exist, resistance can emerge. This study explored the potential of natural products to identify novel dual IDH inhibitors. Methods: In silico screening of the COCONUT database was performed using Lipinski's Rule of Five. Pharmacophore modeling identified crucial features for IDH binding. Docking simulations with Glide (Schrödinger) assessed binding affinity, followed by MM-GBSA calculations for free energy estimation. The most promising candidate underwent ADME/T and toxicity analysis. Finally, molecular dynamics (MD) simulations evaluated the stability of protein-ligand complexes and binding interactions, followed by trajectory analysis using dynamical cross-correlation matrix (DCCM) and principal component analysis (PCA). Results: Ternstroside D (CNP0166496) emerged as a potential dual inhibitor of IDH1 and IDH2 mutations. Docking and MM-GBSA analyses showed strong affinities with IDH1 (-14.2, -84.45 kcal/mol) and IDH2 (-16.8, -60.73 kcal/mol), exceeding those of reference inhibitors GSK321A (-9.6 kcal/mol) and Enasidenib (-8.9 kcal/mol). Key hydrogen-bond interactions with catalytic residues and stable binding during MD simulations support its dual mechanism. ADME/T predictions indicated drug-like properties and a favorable safety profile, highlighting Ternstroside D as a natural scaffold with superior binding compared with existing IDH inhibitors. Conclusion: This in silico study provides compelling evidence for Ternstroside D (CNP0166496) as a promising dual inhibitor for IDH1 and IDH2 mutations in AML. Furthermore, in vitro and in vivo studies are warranted to validate these findings.
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