title: Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets.
abstract: BACKGROUND
  Assessing compound toxicity at early stages of the drug discovery process is a crucial task to dismiss drug candidates likely to fail in clinical trials. Screening drug candidates against structural alerts, i.e. chemical fragments associated to a toxicological response prior or after being metabolized (bioactivation), has proved a valuable approach for this task. During the last decades, diverse algorithms have been proposed for the automatic derivation of structural alerts from categorical toxicity data sets.
  
  
  CONCLUSIONS
  Here, the python library bioalerts is presented, which comprises functionalities for the automatic derivation of structural alerts from categorical (dichotomous), e.g. toxic/non-toxic, and continuous bioactivity data sets, e.g. [Formula: see text] or [Formula: see text] values. The library bioalerts relies on the RDKit implementation of the circular Morgan fingerprint algorithm to compute chemical substructures, which are derived by considering radial atom neighbourhoods of increasing bond radius. In addition to the derivation of structural alerts, bioalerts provides functionalities for the calculation of unhashed (keyed) Morgan fingerprints, which can be used in predictive bioactivity modelling with the advantage of allowing for a chemically meaningful deconvolution of the chemical space. Finally, bioalerts provides functionalities for the easy visualization of the derived structural alerts.
authors.affiliation: Unité de Bioinformatique Structurale, CNRS UMR 3825, Département de Biologie Structurale et Chimie, Institut Pasteur, 25, rue du Dr. Roux, 75015 Paris, France.
authors.forename: Isidro
authors.initials: I
authors.lastname: Cortes-Ciriano
keywords: Bioactivity; Circular fingerprints; Morgan fingerprints; Structural alerts; Toxicology