CANDI: a web server for predicting molecular targets and pathways of cannabis-based therapeutics
Srinivasan Ekambaram, Jian Wang, Nikolay V. Dokholyan

TL;DR
CANDI is a web tool that predicts molecular targets and pathways of cannabis compounds to help develop targeted cannabis-based therapies for diseases like pain and cancer.
Contribution
CANDI is a novel web server that predicts molecular targets and pathways of cannabis formulations using a deep learning model.
Findings
CANDI identified numerous molecular targets of cannabis compounds involved in pain, inflammation, and cancer pathways.
The tool enables researchers to predict targets and pathways for any cannabis formulation, aiding in targeted therapy development.
CANDI bridges traditional cannabis use with modern drug development through computational analysis.
Abstract
Cannabis sativa L. with a rich history of traditional medicinal use, has garnered significant attention in contemporary research for its potential therapeutic applications in various human diseases, including pain, inflammation, cancer, and osteoarthritis. However, the specific molecular targets and mechanisms underlying the synergistic effects of its diverse phytochemical constituents remain elusive. Understanding these mechanisms is crucial for developing targeted, effective cannabis-based therapies. To investigate the molecular targets and pathways involved in the synergistic effects of cannabis compounds, we utilized DRIFT, a deep learning model that leverages attention-based neural networks to predict compound-target interactions. We considered both whole plant extracts and specific plant-based formulations. Predicted targets were then mapped to the Reactome pathway database to…
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Taxonomy
TopicsCannabis and Cannabinoid Research · GABA and Rice Research · Psychedelics and Drug Studies
