Brief. Bioinformatics - Similarity-based machine learning methods for predicting drug-target interactions: a brief review.

Tópicos

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Resumo

Computationally predicting drug-target interactions is useful to select possible drug (or target) candidates for further biochemical verification. We focus on machine learning-based approaches, particularly similarity-based methods that use drug and target similarities, which show relationships among drugs and those among targets, respectively. These two similarities represent two emerging concepts, the chemical space and the genomic space. Typically, the methods combine these two types of similarities to generate models for predicting new drug-target interactions. This process is also closely related to a lot of work in pharmacogenomics or chemical biology that attempt to understand the relationships between the chemical and genomic spaces. This background makes the similarity-based approaches attractive and promising. This article reviews the similarity-based machine learning methods for predicting drug-target interactions, which are state-of-the-art and have aroused great interest in bioinformatics. We describe each of these methods briefly, and empirically compare these methods under a uniform experimental setting to explore their advantages and limitations.

Resumo Limpo

comput predict drugtarget interact use select possibl drug target candid biochem verif focus machin learningbas approach particular similaritybas method use drug target similar show relationship among drug among target respect two similar repres two emerg concept chemic space genom space typic method combin two type similar generat model predict new drugtarget interact process also close relat lot work pharmacogenom chemic biolog attempt understand relationship chemic genom space background make similaritybas approach attract promis articl review similaritybas machin learn method predict drugtarget interact stateoftheart arous great interest bioinformat describ method briefli empir compar method uniform experiment set explor advantag limit

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