The linker representations with reduced dimensionality. Firstly, a pre-processing step to reduce the dimensionality of Mordred linker features is adopted by removing descriptors that are highly correlated with each other (describe similar properties), and those exhibit minimal changes across different instances (do not differentiate between instances). This process specifically drops linker feature columns with Pearson correlation greater than 0. . The length of the fingerprint for ECFPs is modifiable through the hyperparameter "bits", which is associated with the final hash process that converts the chemical information into a fixed-length binary vector. In pursuit of balanced chemical component representations, models are trained using ECFPs linker features generated with 256 bits (refer to Figure -S15, Table ), generating a further 21 models.