  Predicting the novel effects of drugs based on information about approved
drugs can be regarded as a recommendation system. Matrix factorization is one
of the most used recommendation systems and various algorithms have been
devised for it. A literature survey and summary of existing algorithms for
predicting drug effects demonstrated that most such methods, including
neighborhood regularized logistic matrix factorization, which was the best
performer in benchmark tests, used a binary matrix that considers only the
presence or absence of interactions. However, drug effects are known to have
two opposite aspects, such as side effects and therapeutic effects. In the
present study, we proposed using neighborhood regularized bidirectional matrix
factorization (NRBdMF) to predict drug effects by incorporating
bidirectionality, which is a characteristic property of drug effects. We used
this proposed method for predicting side effects using a matrix that considered
the bidirectionality of drug effects, in which known side effects were assigned
a positive label (plus 1) and known treatment effects were assigned a negative
(minus 1) label. The NRBdMF model, which utilizes drug bidirectional
information, achieved enrichment of side effects at the top and indications at
the bottom of the prediction list. This first attempt to consider the
bidirectional nature of drug effects using NRBdMF showed that it reduced false
positives and produced a highly interpretable output.
