to 'MolProps') resulting in fingerprints with a length of the sum of both feature sets (in the case of MolPropsAPFP, 1113 + 256 = 1369). Every feature set was subsequently Znormalized to zero mean and sklearn.decomposition.PCA was used to reduce dimensionality using a principal component analysis, and retaining principal components contributing up to 95% of the variance.