title: Applying machine learning techniques for ADME-Tox prediction: a review.
abstract: BACKGROUND
  Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safety were considered some of the major causes of clinical failures in the development of new chemical entities. In this context, machine learning (ML) techniques have been often used in ADME-Tox studies due to the existence of compounds with known pharmacokinetic properties available for generating predictive models.
  
  
  METHODS
  This review examines the growth in the use of some ML techniques in ADME-Tox studies, in particular supervised and unsupervised techniques. Also, some critical points (e.g., size of the data set and type of output variable) must be considered during the generation of models that relate ADME-Tox properties and biological activity.
  
  
  CONCLUSIONS
  ML techniques have been successfully employed in pharmacokinetic studies, helping the complex process of designing new drug candidates from the use of reliable ML models. An application of this procedure would be the prediction of ADME-Tox properties from studies of quantitative structure-activity relationships or the discovery of new compounds from a virtual screening using filters based on results obtained from ML techniques.
authors.affiliation: Federal University of ABC (UFABC), Centre for Natural Sciences and Humanities , Santa Adélia Street, 166, Bangu, Santo André -SP , Brazil.
authors.forename: Vinícius Gonçalves
authors.initials: VG
authors.lastname: Maltarollo
authors.forename: Jadson Castro
authors.initials: JC
authors.lastname: Gertrudes
authors.forename: Patrícia Rufino
authors.initials: PR
authors.lastname: Oliveira
authors.forename: Kathia Maria
authors.initials: KM
authors.lastname: Honorio
mesh_terms: D001185:Artificial Intelligence; D015195:Drug Design; D006801:Humans; D008954:Models, Biological; D021281:Quantitative Structure-Activity Relationship; D015262:Xenobiotics / Q000737:chemistry / Q000493:pharmacokinetics* / Q000633:toxicity
chemical_list: D015262:Xenobiotics
keywords: absorption, distribution, metabolism, excretion and toxicity of xenobiotics; in silico drug design; machine learning; molecular modeling; pharmacokinetics; quantitative structure–activity relationships