Machine learning models for classification tasks related to drug safety

Author:

Rácz AnitaORCID,Bajusz DávidORCID,Miranda-Quintana Ramón AlainORCID,Héberger KárolyORCID

Abstract

AbstractIn this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015–2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood–brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts. Graphical abstract

Funder

Ministry for Innovation and Technology of Hungary

Nemzeti Kutatási és Technológiai Hivatal

Magyar Tudományos Akadémia

ELKH Research Centre for Natural Sciences

Publisher

Springer Science and Business Media LLC

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Drug Discovery,Molecular Biology,General Medicine,Information Systems,Catalysis

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