Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications
Jonathan Fine, Rachel M. Lackner, Ram Samudrala, Gaurav Chopra
Scientific Reports September 11, 2019 DOI: 10.1038/s41598-019-49515-0 (opens in new tab) via OpenAlex
Summary
AI-generated from the abstractA computational platform called CANDO, which analyzes structural compound-proteome interaction signatures for 3,733 compounds against 48,278 proteins, was used to predict therapeutic properties of 428 psychoactive compounds from the phenylethylamine, tryptamine, and cannabinoid classes for mental health indications. The predictions showed that these psychoactives ranked among the top for a significant fraction of mental health indications, with a significant preference over non-mental health indications compared to randomized controls. Specific analyses examined tryptamines for sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. The approach may guide identification of novel therapies for mental health and neurological disorders.
Study at a glance
| Characteristics | Computational platform development and analysis Randomized Peer reviewed |
|---|---|
| Keywords | Mental health Computer science Computational biology Data science Bioinformatics |
| Citations | 33 |
| Key finding | 428 psychoactive compounds from three chemical classes are among the top-ranked predictions for a significant fraction of mental health indications, with a significant preference over non-mental health indications relative to randomized controls. |
Abstract
We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.