BitterMatch: recommendation systems for matching molecules with bitter taste receptors

Eitan Margulis, Yuli Slavutsky, Tatjana Lang, Maik Behrens, Yuval Benjamini, Masha Y. Niv*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Bitterness is an aversive cue elicited by thousands of chemically diverse compounds. Bitter taste may prevent consumption of foods and jeopardize drug compliance. The G protein-coupled receptors for bitter taste, TAS2Rs, have species-dependent number of subtypes and varying expression levels in extraoral tissues. Molecular recognition by TAS2R subtypes is physiologically important, and presents a challenging case study for ligand-receptor matchmaking. Inspired by hybrid recommendation systems, we developed a new set of similarity features, and created the BitterMatch algorithm that predicts associations of ligands to receptors with ~ 80% precision at ~ 50% recall. Associations for several compounds were tested in-vitro, resulting in 80% precision and 42% recall. The encouraging performance was achieved by including receptor properties and integrating experimentally determined ligand-receptor associations with chemical ligand-to-ligand similarities. BitterMatch can predict off-targets for bitter drugs, identify novel ligands and guide flavor design. The novel features capture information regarding the molecules and their receptors, which could inform various chemoinformatic tasks. Inclusion of neighbor-informed similarities improves as experimental data mounts, and provides a generalizable framework for molecule-biotarget matching. Graphical Abstract: [Figure not available: see fulltext.].

Original languageAmerican English
Article number45
JournalJournal of Cheminformatics
Volume14
Issue number1
DOIs
StatePublished - Dec 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Bitter
  • Drugs
  • GPCRs
  • Machine learning
  • Taste

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