Bioactivity descriptors for uncharacterized chemical compounds

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  • dc.contributor.author Bertoni, Martino
  • dc.contributor.author Duran-Frigola, Miquel, 1985-
  • dc.contributor.author Badia-I-Mompel, Pau
  • dc.contributor.author Pauls, Eduardo
  • dc.contributor.author Orozco Ruiz, Modesto
  • dc.contributor.author Guitart Pla, Oriol
  • dc.contributor.author Alcalde, Víctor
  • dc.contributor.author Diaz, Víctor M.
  • dc.contributor.author Berenguer-Llergo, Antonio
  • dc.contributor.author Brun-Heath, Isabelle
  • dc.contributor.author Villegas, Núria
  • dc.contributor.author García de Herreros, Antonio
  • dc.contributor.author Aloy, Patrick, 1972-
  • dc.date.accessioned 2021-08-06T06:04:18Z
  • dc.date.available 2021-08-06T06:04:18Z
  • dc.date.issued 2021
  • dc.description.abstract Chemical descriptors encode the physicochemical and structural properties of small molecules, and they are at the core of chemoinformatics. The broad release of bioactivity data has prompted enriched representations of compounds, reaching beyond chemical structures and capturing their known biological properties. Unfortunately, bioactivity descriptors are not available for most small molecules, which limits their applicability to a few thousand well characterized compounds. Here we present a collection of deep neural networks able to infer bioactivity signatures for any compound of interest, even when little or no experimental information is available for them. Our signaturizers relate to bioactivities of 25 different types (including target profiles, cellular response and clinical outcomes) and can be used as drop-in replacements for chemical descriptors in day-to-day chemoinformatics tasks. Indeed, we illustrate how inferred bioactivity signatures are useful to navigate the chemical space in a biologically relevant manner, unveiling higher-order organization in natural product collections, and to enrich mostly uncharacterized chemical libraries for activity against the drug-orphan target Snail1. Moreover, we implement a battery of signature-activity relationship (SigAR) models and show a substantial improvement in performance, with respect to chemistry-based classifiers, across a series of biophysics and physiology activity prediction benchmarks.
  • dc.description.sponsorship We would like to thank the SB&NB lab members for their support and helpful discussions. We are grateful to T.O. Botelho, I. Ramos, and C. Gonzalez for giving us access to the IRB Barcelona and Prestwick libraries. P.A. acknowledges the support of the Generalitat de Catalunya (RIS3CAT Emergents CECH: 001-P-001682 and VEIS: 001-P-001647), the Spanish Ministerio de Economía y Competitividad (BIO2016-77038-R), the European Research Council (SysPharmAD: 614944), and the European Commission (RiPCoN: 101003633). A.G.d.H. acknowledges support by Agencia Estatal de Investigación (AEI) and Fondos FEDER (PID2019-104698RB-I00).
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Bertoni M, Duran-Frigola M, Badia-I-Mompel P, Pauls E, Orozco-Ruiz M, Guitart-Pla O, Alcalde V, Diaz VM, Berenguer-Llergo A, Brun-Heath I, Villegas N, de Herreros AG, Aloy P. Bioactivity descriptors for uncharacterized chemical compounds. Nat Commun. 2021;12(1):3932. DOI: 10.1038/s41467-021-24150-4
  • dc.identifier.doi http://dx.doi.org/10.1038/s41467-021-24150-4
  • dc.identifier.issn 2041-1723
  • dc.identifier.uri http://hdl.handle.net/10230/48319
  • dc.language.iso eng
  • dc.publisher Nature Research
  • dc.relation.ispartof Nat Commun. 2021;12(1):3932
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/614944
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/BIO2016-77038-R
  • dc.rights © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.keyword Cheminformatics
  • dc.subject.keyword Machine learning
  • dc.subject.keyword Networks and systems biology
  • dc.title Bioactivity descriptors for uncharacterized chemical compounds
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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