Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding

Gwanghoon Jang, Sungjoon Park, Sanghoon Lee, Sunkyu Kim, Sejeong Park, Jaewoo Kang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Motivation: Identifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signatures induced by compounds. However, these approaches fail to reveal MoAs in the absence of actual compound signatures. Results: We present MoAble, which predicts MoAs without requiring compound signatures. We train a deep learning-based coembedding model to map compound signatures and compound structure into the same embedding space. The model generates low-dimensional compound signature representation from the compound structures. To predict MoAs, pathway enrichment analysis is performed based on the connectivity between embedding vectors of compounds and those of genetic perturbation. Results show that MoAble is comparable to the methods that use actual compound signatures. We demonstrate that MoAble can be used to reveal MoAs of novel compounds without measuring compound signatures with the same prediction accuracy as that with measuring them.

Original languageEnglish
Pages (from-to)I376-I382
JournalBioinformatics
Volume37
DOIs
Publication statusPublished - 2021 Jul 1

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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