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. AVAILABILITY AND IMPLEMENTATION: MoAble is available at https://github.com/dmis-lab/moable. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. AVAILABILITY AND IMPLEMENTATION: MoAble is available at https://github.com/dmis-lab/moable. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Yan Ren; Siva Sivaganesan; Nicholas A Clark; Lixia Zhang; Jacek Biesiada; Wen Niu; David R Plas; Mario Medvedovic Journal: Bioinformatics Date: 2020-09-15 Impact factor: 6.937
Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205
Authors: Maxim V Kuleshov; Matthew R Jones; Andrew D Rouillard; Nicolas F Fernandez; Qiaonan Duan; Zichen Wang; Simon Koplev; Sherry L Jenkins; Kathleen M Jagodnik; Alexander Lachmann; Michael G McDermott; Caroline D Monteiro; Gregory W Gundersen; Avi Ma'ayan Journal: Nucleic Acids Res Date: 2016-05-03 Impact factor: 16.971