Benchmark Dataset and Sample Formulation

We utilized the same dataset as the previous study (Cheng et al., 2017b) to facilitate model comparison. This dataset consists of 3,883 drugs, and each drug is labeled with at least one or more of 14 main ATC classes. It is a tidy dataset where no missing value and contradictory record. The UpSet visualization technique (Lex et al., 2014) was used for quantitative analysis of interactions of label sets.

Then, we adopted the same method provided by (Cheng et al., 2017b) to represent the drug samples. The dataset can be formulated in set notation as the union of elements in each class: S=S1∪S2…∪S14 (1), and a sample D can be represented by concatenating the following three types of features.

A 14-dimentional vector, D ^Int = [Φ₁Φ₂Φ₃ … Φ₁₄]^T (2), which represents its maximum interaction score Φ_i (Kotera et al., 2012) with the drugs in each of the 14 Si.

A 14-dimentional vector, D ^StrSim = [Ψ₁Ψ₂Ψ_{3 …} Ψ₁₄]^T (3) which represents its maximum structural similarity score Ψ_i (Kotera et al., 2012) with the drugs in each of the 14 Si.

A 14-dimentional vector, D ^FigSim = [T₁T₂T₃ … T₁₄]^T (4), which represents its molecular fingerprint similarity score T_i (Xiao et al., 2013) with the drugs in each of the 14 Si.

Therefore, a given drug D is formulated by:

Where ⊕ represents the symbol for orthogonal sum and where

For more details, refer to Cheng et al. (2017b).