Bioactivity descriptors for uncharacterized chemical compounds

Bertoni, Martino; Duran-Frigola, Miquel, 1985-; Badia-I-Mompel, Pau; Pauls, Eduardo; Orozco, Modesto; Guitart Pla, Oriol; Alcalde, Víctor; Diaz, Víctor M.; Berenguer-Llergo, Antonio; Brun-Heath, Isabelle; Villegas, Núria; García de Herreros, Antonio; Aloy, Patrick, 1972-

doi:http://dx.doi.org/10.1038/s41467-021-24150-4

Bioactivity descriptors for uncharacterized chemical compounds

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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

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.