We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform thatconstructs and analyzes the structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins to infer homology of drug behavior at the proteomic level in a shotgun manner. We applied theCANDO platform to predict putative therapeutic properties of 428 psychoactive compounds belonging to thephenylethylamine, tryptamine, and cannabinoid chemical classes for the treatment of mental health indications.Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significantfraction of mental health indications, demonstrating a significant preference for treating such indications overnon-mental health indications, relative to randomized controls. Additionally, we examined the inverse questionto determine that mental health indications are the top-ranked indications for psychoactive compounds. Toverify these results with known literature, we analyzed the use of specific tryptamines for the treatment ofsleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Finally, we havedeveloped a new methodology to determine the relationship between different indications at both the proteomicand therapeutic levels and evaluated these relationships using these psychoactive compounds for the selectedmental health indications. Not only do our findings reveal already known relationships between mental healthindications, but they also suggest new ones that can be investigated in future works. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indicationsand provide an understanding of their causal basis on a detailed mechanistic level. Given the recent change inperception of psychoactive compounds as treatments for a myriad of psychological disorders, we hope thatthese predictions can be used to provide new avenues for preclinical drug development for mental health andother neurological disorders.

Computational chemoproteomics to understand the role of psychoactives in treating mental health indications

ACS Fall 2020 Virtual Meeting

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