Similarly-Based Machine Learning Approaches for Predicting Drug-Target Interactions

摘要

Computationally predicting drug-target interactions is useful to discover potential new drugs. Currently, promising machine learning approaches for this issue use not only known drug-target interactions but also drug and target similarities. This idea can be well-accepted pharmacologically, since the two types of similarities correspond to two recently advocated concepts, so-called, the chemical space and the genomic space. I will start this talk by describing detailed background on the problem of predicting drug-target interactions, particularly why similarity-based approaches have been paid attention to now. I will then move on to the existing approaches and their bottlenecks and further present recent factor model-based approaches, which allow low-rank approximation of given matrices, by which the issues of the past methods can be properly considered. Also I note that the problem setting of similarity-based predicting of drug-target interactions is very general, in the sense of binary relations between two sets of events, in which events have similarities each other. This general setting can be found in many applications, such as recommender systems.