Abstract: Allosteric regulation plays a crucial role in modulating protein functions and represents a promising strategy in drug development, offering enhanced specificity and reduced toxicity compared to traditional active site inhibition. Existing computational methods for predicting allosteric sites on proteins often rely on static protein surface pocket features, normal mode analysis, or extensive molecular dynamics simulations encompassing both the protein function modulator and the protein itself. In this study, we introduce an innovative methodology that employs a per amino acid residue classifier to distinguish allosteric site-forming residues (AFRs) from non-allosteric, or free residues (FRs). Our model, STINGAllo, exhibits robust performance, achieving Distance Center Center (DCC) success rate when all AFRs were predicted within pockets identified by FPocket, overall DCC, F1 score, and a Matthews correlation coefficient…