Master store of all sample data features×samples F Names of (selected) features features G Group references corresponding to SMILES of each sample samples sp Mask of features for dimensionality reduction methods features X Data to fit samples×features Y Target variables for all samples samples Features (F) were filtered for low variance of less than 5% or high correlation between descriptors (i.e. if their pairwise correlation is greater than 97%, eliminate the descriptor with the highest correlation among all descriptors). This process is applied at the start of every experiment, and as each experiment identifies the strongest descriptors, the pool is progressively reduced. In this way, the screening out of less useful descriptors with low confidence is achieved.