Scheme 1 | Computational pipeline workflow a. Data is first processed using conventional techniques and featurized using cheminformatics libraries to accurately capture chemical information. Following this, feature selection is carried out to find the optimum subset of features that can maximize model performance. b. These subsets are then used to train different modelsnamely random forests, support vector machines, and gradient boosted machineson a coarse grid of hyperparameters, evaluated across multiple metrics. The best-performing model is further optimized on a fine grid of hyperparameters. c. The final model is first validated on unseen test data and then used to screen the CSD for potentially biocompatible MOF candidates for drug delivery.