title: Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field.
abstract: BACKGROUND
  There has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software.
  
  
  RESULTS
  The Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery.
  
  
  CONCLUSIONS
  Open Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT's source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt).
authors.affiliation: Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, 02-106 Warsaw, Poland.
authors.forename: Maciej
authors.initials: M
authors.lastname: Wójcikowski
authors.affiliation: Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, 02-106 Warsaw, Poland ; Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland.
authors.forename: Piotr
authors.initials: P
authors.lastname: Zielenkiewicz
authors.affiliation: Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, 02-106 Warsaw, Poland ; Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland.
authors.forename: Pawel
authors.initials: P
authors.lastname: Siedlecki
keywords: Machine learning; Programming; Receptor-ligand interactions; Scoring function; Statistical methods; Toolkit; Virtual screening