Brief. Bioinformatics - Toward more realistic drug-target interaction predictions.


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A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug-target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug-target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug-target interaction prediction results. We also suggest guidelines on how to make the supervised drug-target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug-target interactions for kinase inhibitors.

Resumo Limpo

number supervis machin learn model recent introduc predict drugtarget interact base chemic structur genom sequenc inform although model offer improv mean mani network pharmacolog applic reposit drug new therapeut use predict model often construct evalu over simplifi set reflect reallif problem practic applic use quantit drugtarget bioactiv assay kinas inhibitor well popular benchmark data set binari drugtarget interact enzym ion channel nuclear receptor g proteincoupl receptor target illustr effect four factor may lead dramat differ predict result problem formul standard binari classif realist regress formul ii evalu data set drug target famili applic use case iii evalu procedur simpl nest crossvalid iv experiment set whether train test set share common drug target drug target neither factor taken consider avoid report overoptimist drugtarget interact predict result also suggest guidelin make supervis drugtarget interact predict studi realist term model formul evalu setup better address inher complex predict task practic applic well novel benchmark data set captur continu natur drugtarget interact kinas inhibitor

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