Machine Learning-Driven Discovery of Potent IDH1 Mutant Inhibitors from Ultra-Large Ligand Libraries for Targeting Malignant Glioma

Glioblastoma (GBM) represents one of the most lethal and therapy-resistant forms of brain tumors, characterized by high heterogeneity, metabolic reprogramming, and recurrence. In the current study, we aimed to identify novel small-molecule inhibitors targeting mutant isocitrate dehydrogenase 1 (IDH1), a crucial enzyme involved in GBM tumor metabolism. For this aim, machine learning based quantitative structure-activity relationship (QSAR) modeling was combined with structure-based e-pharmacophore screening to virtually screen ultra-large chemical libraries containing around 157 million compounds. The best hits were selected based on docking score, predicted pIC 50 value, and molecular mechanics/generalized born surface area (MM/GBSA) binding energy calculations. Furthermore, Molecular dynamics (MD) simulations were conducted to validate the hits. In total, 36 compounds were subjected to short MD simulations (10 ns), and 16 molecules showing low binding free energies (below -90 kcal/mol) were further analyzed through long MD simulations (100 ns). Among these, eleven hits were purchased and experimentally tested on human glioblastoma U87 and U251 cell lines. Our experimental results showed that five of the tested compounds (Hits 1 , 4 , 5 , 6 , and 7 ) significantly reduced spheroid formation by nearly 90% and inhibited cell proliferation with IC 50 values between 12 and 25 μM. Moreover, these hits strongly decreased the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), by up to 62% and 55%, respectively, indicating inhibition of both mitochondrial respiration and glycolysis. Furthermore, Western blot and qRT-PCR analyses revealed down-regulation of glycolytic enzymes (HK2, PKM2, GLUT1) and stemness markers (CD133, Lgr5, ALDH1). Moreover, steered molecular dynamics (sMD) and free energy perturbation (FEP) analyses confirmed the stable interactions of these compounds at the IDH1 mutant active site. This multi-stage in silico – in vitro approach allowed the identification of metabolically disruptive mutant IDH1 inhibitors that suppress glycolysis, mitochondrial respiration, and cancer stemness in glioblastoma cells. These compounds represent promising scaffolds for the development of next-generation GBM therapeutics.