PKM2Pred: An AI Tool for Rapid Identification and Potency Estimation of PKM2-Targeting Anticancer Compounds

Cancer continues to pose a major global health challenge due to its metabolic complexity. Pyruvate Kinase M2 (PKM2), a key glycolytic enzyme, is central to tumor progression and metastasis. To facilitate targeted drug discovery, we introduce PKM2Pred (https://pkm2pred.vercel.app/), a machine learning based freely accessible web server that classifies compounds as activators, inhibitors, or decoys and predicts their AC₅₀ range. Built on a Random Forest classifier, the model achieved 94% accuracy and a Matthews Correlation Coefficient of 90.02%. A bootstrapped regression model estimated bioactivity ranges with confidence intervals, offering flexibility between prediction and range. The top three key molecular descriptors, such as WTPT-5, SRW9, and nHeteroRing, emerged as the most important statistical descriptors based on their percentage importance of 12.5, 8.2, and 5.8, respectively. Thus, PKM2Pred offers rapid, reliable, and cost-effective computational insight for anticancer drug discovery.