  Motivation: Computational prediction of multiple-type drug-drug interaction
(DDI) helps reduce unexpected side effects in poly-drug treatments. Although
existing computational approaches achieve inspiring results, they ignore that
the action of a drug is mainly caused by its chemical substructures. In
addition, their interpretability is still weak. Results: In this paper, by
supposing that the interactions between two given drugs are caused by their
local chemical structures (sub-structures) and their DDI types are determined
by the linkages between different substructure sets, we design a novel
Substructure-ware Tensor Neural Network model for DDI prediction (STNN-DDI).
The proposed model learns a 3-D tensor of (substructure, in-teraction type,
substructure) triplets, which characterizes a substructure-substructure
interaction (SSI) space. According to a list of predefined substructures with
specific chemical meanings, the mapping of drugs into this SSI space enables
STNN-DDI to perform the multiple-type DDI prediction in both transductive and
inductive scenarios in a unified form with an explicable manner. The
compar-ison with deep learning-based state-of-the-art baselines demonstrates
the superiority of STNN-DDI with the significant improvement of AUC, AUPR,
Accuracy, and Precision. More importantly, case studies illustrate its
interpretability by both revealing a crucial sub-structure pair across drugs
regarding a DDI type of interest and uncovering interaction type-specific
substructure pairs in a given DDI. In summary, STNN-DDI provides an effective
approach to predicting DDIs as well as explaining the interaction mechanisms
among drugs.
