Bioactivity descriptors for uncharacterized chemical compounds

Martino Bertoni, Miquel Duran-Frigola (), Pau Badia-i-Mompel, Eduardo Pauls, Modesto Orozco-Ruiz, Oriol Guitart-Pla, Víctor Alcalde, Víctor M. Diaz, Antoni Berenguer-Llergo, Isabelle Brun-Heath, Núria Villegas, Antonio García Herreros and Patrick Aloy ()
Additional contact information
Martino Bertoni: The Barcelona Institute of Science and Technology
Miquel Duran-Frigola: The Barcelona Institute of Science and Technology
Pau Badia-i-Mompel: The Barcelona Institute of Science and Technology
Eduardo Pauls: The Barcelona Institute of Science and Technology
Modesto Orozco-Ruiz: The Barcelona Institute of Science and Technology
Oriol Guitart-Pla: The Barcelona Institute of Science and Technology
Víctor Alcalde: The Barcelona Institute of Science and Technology
Víctor M. Diaz: Universitat Pompeu Fabra (UPF)
Antoni Berenguer-Llergo: The Barcelona Institute of Science and Technology
Isabelle Brun-Heath: The Barcelona Institute of Science and Technology
Núria Villegas: The Barcelona Institute of Science and Technology
Antonio García Herreros: Universitat Pompeu Fabra (UPF)
Patrick Aloy: The Barcelona Institute of Science and Technology

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: 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.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41467-021-24150-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24150-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-24150-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().