Related Experiment Video
Updated: Jun 14, 2025

Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods
Published on: August 4, 2022
Artificial intelligence approaches for anti-addiction drug discovery
Dong Chen1, Jian Jiang1,2, Nicole Hayes1
1Department of Mathematics, Michigan State University MI 48824 USA weig@msu.edu.
None:
Drug addiction remains a complex global public health challenge, with traditional anti-addiction drug discovery hindered by limited efficacy and slow progress in targeting intricate neurochemical systems. Advanced algorithms within artificial intelligence (AI) present a transformative solution that boosts both speed and precision in therapeutic development. This review examines how artificial intelligence serves as a crucial element in developing anti-addiction medications by targeting the opioid system along with dopaminergic and GABAergic systems, which are essential in addiction pathology. It identifies upcoming trends promising in studying less-researched addiction-linked systems through innovative general-purpose drug discovery techniques. AI holds the potential to transform anti-addiction research by breaking down conventional limitations, which will enable the development of superior treatment methods.
More Related Videos
Related Concept Videos
Drug Abuse and Addiction: Pharmacological Phenomena
Drug Discovery: Overview
Structure-Activity Relationships and Drug Design
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
Drug-Receptor Interaction: Agonist
Agonists can bind to receptors in different ways. Some agonists bind directly to the receptor's active site, mimicking the endogenous...
Drug Dependence
Cognitive Enhancers: Cholinesterase Inhibitors and NMDA Receptor Antagonists

