A team of computational and molecular biologists at the University of Tartu has combined machine learning with rigorous experimental validation to identify a brand-new inhibitor of vascular endothelial growth factor receptor 2 (VEGFR-2), a kinase that drives the growth of blood vessels feeding tumours. The newly discovered molecule, built around a phthalazine core decorated with a naphthalene fragment, showed sub-micromolar inhibition of the isolated enzyme and measurable antiproliferative activity in glioblastoma and neuroglioma cell lines, while sparing a non-malignant reference cell model. The work, published in Results in Chemistry, is notable not only for the hit compound itself but for the unusually disciplined pipeline that produced it: a curated, chemically diverse dataset, a transparent and explainable model, and a willingness to test predictions in the laboratory rather than leave them on the page.
The biological target at the heart of the study is one of the most heavily exploited in modern oncology. VEGF signalling is the master switch of tumour angiogenesis, the process by which cancers recruit their own blood supply, and blocking it has produced some of the first triumphs of targeted therapy. The monoclonal antibody bevacizumab, approved by the FDA in 2004 for metastatic colorectal cancer, starves tumours by intercepting VEGF itself, while a growing family of small-molecule tyrosine kinase inhibitors including sorafenib, sunitinib, pazopanib, regorafenib, cabozantinib, axitinib, tivozanib, vandetanib, lenvatinib and nintedanib jam the intracellular signalling machinery of VEGFR-2. Yet these drugs have been most successful in liver, gastrointestinal, renal and thyroid malignancies, and their efficacy in brain tumours has remained stubbornly disappointing, leaving a clear opening for better molecules.
Glioblastoma multiforme is the context in which that unmet need is starkest. It is the most common malignant brain tumour, accounting for nearly half of all such cases, and it carries one of the grimmest prognoses in medicine. The current standard of care combines surgery, radiotherapy and the DNA-methylating agent temozolomide, supplemented in some settings by nitrosoureas such as lomustine and carmustine that cross the blood-brain barrier. All of these carry serious toxicities, from haematological suppression to pulmonary and ocular damage. Because glioblastoma neovascularisation is heavily regulated through VEGF and VEGFR-2, the receptor remains an attractive target, and one published patent has already proposed VEGFR-2 blockade as a treatment strategy for sensitive cancers. What has been missing is a reliable way to find new chemical matter for the purpose.
The Tartu researchers began with the VEGFR-2 binding site itself, which presents four pharmacophoric regions that successful inhibitors must engage. A heteroaromatic ring occupies the hinge region of the catalytic ATP binding pocket; a hydrophobic aryl or heteroaryl group fills the space leading toward the Asp-Phe-Gly motif; a hydrogen-bond donor and acceptor linker such as a urea or amine addresses the DFG domain; and, when the receptor adopts its inactive conformation, a deep allosteric hydrophobic pocket opens that confers selectivity. Rather than modelling a single congeneric series, as most prior QSAR efforts have done, the team assembled a dataset of 216 VEGFR-2 inhibitors spanning twelve distinct parent scaffolds, including phthalazines, pyridines, quinazolines, quinoxalines, benzoxazoles, benzothiazoles, coumarins, thiazolidine-2,4-diones, thioureas, nicotinamides, quinolines and isatins, all manually curated from a decade of literature with careful attention to assay consistency, duplicates, salts and isomers.
From this dataset, divided into 173 training and 43 test compounds, the researchers built a five-parameter quantitative structure-activity relationship model using best multiple linear regression with step-forward descriptor selection. The statistical performance was impressive for such a chemically heterogeneous training pool: a coefficient of determination of 0.824 on the training set, a cross-validated value of 0.807, and an external test set correlation of 0.74, with concordance correlation coefficients above 0.85 in both settings. A thousand-step Y-randomisation returned an essentially zero coefficient of determination, confirming that the model captured genuine structure-activity signal rather than chance correlation. Critically, all five descriptors are interpretable. The maximum carbon electrotopological state and the polarizability-weighted Geary autocorrelation contribute positively to activity, the mass-weighted Broto-Moreau autocorrelation and ionisation-potential-weighted Geary term contribute negatively, and a summed nitrogen-hydrogen E-state index rewards the presence of amino linker motifs, mirroring the pharmacophore logic of the binding site.
The model was then turned loose on chemical databases. Drawing on the structure-activity analysis of the most active phthalazine, pyridine and quinazoline derivatives, the team designed five extended scaffolds, or chemotypes, including pyridine variants bearing phenyl substituents and a phthalazine in which a naphthalene moiety, known to confer potent sub-nanomolar VEGFR-2 inhibition, was attached through an amino linker. Screening the MolPort and ZINC15 databases with these chemotypes and ranking candidates by predicted pIC50 yielded eight purchasable compounds with predicted activities between 8.4 and 9.5, all within the model’s applicability domain. Each was then purchased and put to the test in a cell-free VEGFR-2 kinase assay.
One molecule stood out. Compound G, formally N-(naphthalen-2-yl)-4-[4-(prop-2-yn-1-yloxy)phenyl]phthalazin-1-amine, inhibited VEGFR-2 with an IC50 of 0.497 micromolar, verified by high-performance liquid chromatography purification to better than 98 percent purity and confirmed across three independent experiments. Its naphthalene-bearing phthalazine architecture, the researchers note, has no close counterpart among reported VEGFR-2 inhibitors in PubChem, ZINC15 or ChEMBL, making it a genuinely novel chemotype and a credible analogue of the clinical candidate vatalanib. The other seven compounds, despite structural similarity to known actives and confident predictions, fell short, including a methoxy-substituted pyridine that differed from an active literature molecule by a single methyl ether group. That mixed outcome is itself informative, a candid illustration of the gap between prediction and biology.
The cellular results added nuance. In MTT viability assays after 72 hours of treatment, compound G reduced viability of H4 neuroglioma cells with an IC50 of 14.4 micromolar and A172 glioblastoma cells with an IC50 of 27.3 micromolar, both in a concentration-dependent manner. The non-malignant HEK293 reference model showed no significant loss of viability, and the breast cancer line MCF-7 was only marginally affected, hinting at selectivity for glial tumour cells. However, the high-grade U118MG glioblastoma line did not respond significantly, and the authors are careful to explain why: enzyme inhibition in a test tube does not guarantee cellular efficacy, which depends on compound permeability, intracellular target access and compensatory survival pathways. These findings echo earlier work showing that selective VEGFR-2 blockade suppresses VEGF-driven growth in astrocytoma models, while also reflecting the known limitations of anti-angiogenic drugs, which rarely kill tumour cells directly and often deliver modest survival benefits.
The authors position the new phthalazine hit as a starting point rather than a drug. It now requires optimisation, confirmation of target engagement inside cells, ADMET profiling and broader biological validation, and the team argues that pairing VEGFR-2 blockade with inhibition of EGFR, c-Met, BRAF or epigenetic targets such as HDAC could overcome the resistance and vessel co-option that blunt single-target anti-angiogenic therapy. What makes the study a compelling template is its transparency: the QSAR models and curated data are publicly deposited in the QsarDB repository with a digital object identifier, allowing other groups to interrogate, reuse and extend the work. At a moment when artificial intelligence in drug discovery is often oversold, this pipeline, modest dataset, explainable model, honest experimental accounting and a verified novel hit against one of medicine’s most feared cancers, shows what careful, verifiable machine learning can actually deliver.
Subject of Research: Machine learning-guided discovery of a novel phthalazine-based VEGFR-2 inhibitor evaluated in glioblastoma cell lines
Article Title: Discovery of a novel phthalazine-based VEGFR-2 inhibitor by machine learning analysis of diverse chemical space with favourable pharmacophores and its evaluation in glioblastoma cell lines
Article References: Zukic, S., Ivanova, L., Zusinaite, E., Merits, A., & Maran, U. (2026). Discovery of a novel phthalazine-based VEGFR-2 inhibitor by machine learning analysis of diverse chemical space with favourable pharmacophores and its evaluation in glioblastoma cell lines. Results in Chemistry, 30, Article 103884. https://doi.org/10.1016/j.rechem.2026.103884
Image Credits: AI Generated
DOI: 10.1016/j.rechem.2026.103884
Keywords: VEGFR-2, phthalazine, machine learning, QSAR, glioblastoma, angiogenesis, virtual screening, drug discovery, tyrosine kinase inhibitor, naphthalene, cancer therapy, computational chemistry
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Louis Brooks. (September 26, 2026). Machine learning uncovers a novel phthalazine drug candidate against deadly brain tumours. Scienmag. https://scienmag.com/machine-learning-uncovers-a-novel-phthalazine-drug-candidate-against-deadly-brain-tumours/
Louis Brooks. “Machine learning uncovers a novel phthalazine drug candidate against deadly brain tumours.” Scienmag, 26 September 2026, https://scienmag.com/machine-learning-uncovers-a-novel-phthalazine-drug-candidate-against-deadly-brain-tumours/. Accessed 26 September 2026.
Louis Brooks. “Machine learning uncovers a novel phthalazine drug candidate against deadly brain tumours.” Scienmag. September 26, 2026. https://scienmag.com/machine-learning-uncovers-a-novel-phthalazine-drug-candidate-against-deadly-brain-tumours/
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Tags: angiogenesisanti-angiogenic drug developmentCancer Therapychemical diversity in drug designcomputational biology in oncologycomputational chemistrydrug discoveryexperimental validation of cancer drugsexplainable AI in pharmacologyGlioblastomaglioblastoma treatment researchkinase inhibitors for tumor growthMachine learningmachine learning in drug discoverymachine learning pipeline for drug discoverynaphthalenenovel VEGFR-2 inhibitorphthalazinephthalazine-based cancer therapeuticsQSARtargeted therapy for brain tumorstyrosine kinase inhibitorVEGFR-2virtual screening
