TABLE 1.

DDI prediction methods based on machine learning.

Category		Method	Description
Traditional similarity		Vilar et al. (2012), Rus-Rao Zhang et al. (2009), Gottlieb et al. (2012)	Drug A and drug B interact to produce A specific effect, and it is likely that A drug similar to drug A (or drug B) interacts with drug B (or drug A) to produce the same effect
Traditional classification		Li et al. (2015), Jian-Yu et al. (2016), Kastrin et al. (2018)	The prediction task is simulated as a binary classification problem. Drugs interaction and non-interaction pairs were used to construct classification models
Network diffusion	Link prediction	PPIN Cami et al. (2013), Yan et al. (2019), Zhang et al. (2015), Park et al. (2015), Sridhar et al. (2016)	Using drugs as nodes, and their extensive connections and interactions as edges, to predict unknown interactions. Lable propagation, recursive least squares (RSL), traversal of graph and other methods are also used for link prediction
	Graph embedding	Decagon Wu et al., 2020, Feng et al. (2020), Ma et al. (2018), Liu et al. (2019), DeepDDI Ryu et al. (2018), Lee et al. (2019), Hou et al. (2019), Yifan et al. (2020)	Transform the graph into a low-dimensional space in which the information about the structure diagram is preserved. Automatically learn node representation in low dimensional space for prediction
Matrix factorization		IPF Vilar et al. (2013), MRMF Zhang et al. (2018), Shtar et al. (2019), ISCMF Rohani et al. (2020), DDINMF Yu et al. (2018), TMFUF Shi et al. (2018)	Matrix factorization decomposes the known DDI matrix into several potential matrices constrained by collective similarity, and then reconstructs the potential matrix to obtain a new interaction matrix
Ensemble-based approach		MLKNN Zhang et al. (2017)	Combine multiple methods to predict unknown DDI.
Based on literature		Tari et al. (2010), Tatonetti et al. (2012b), Kolchinsky et al. (2013)	Firstly, statistical or text mining methods are used to extract the reasonable relationship between drugs from unstructured data sources, and then machine learning methods are used to predict the unknown drug-drug interaction from the extracted drug-drug interaction information