Multiple ML models were evaluated using different fingerprints along with the 27 remaining descriptors in a nonlinear Support Vector Machine (SVM) regressor. The data was randomly split into training and testing sets of 75% and 25%, respectively. All fingerprints were standardized to a length of 2048 bits-including ECFP, Topological, FCFP, Atompairs, Pattern, and Layered fingerprints-with the exception of Avalon (512-bits) and the MACCS keys (167-bits) fingerprints. Further exploration of diverse ML models was conducted on the curated STADS, employing Ridge Linear, Bayesian Ridge Linear, Random Forest (RF), Huber, and K-Nearest Neighbors (KNN) regressors. Hyperparameter optimization was applied to all models, and their performance was evaluated using the Spearman correlation coefficient for the testing set (𝑟 𝑡𝑒𝑠𝑡 2 ) and training set(𝑟 𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔