Artificial Intelligence Approaches for Anti-Addiction Drug Discovery

TL;DR

This paper reviews how artificial intelligence techniques are transforming anti-addiction drug discovery by improving efficiency, accuracy, and overcoming traditional research barriers in identifying and developing new therapeutics.

Contribution

It systematically analyzes AI's role across the drug discovery pipeline for anti-addiction therapies, highlighting novel applications and potential to revolutionize the field.

Findings

AI accelerates data collection and analysis in drug discovery.

AI improves target identification accuracy.

AI enhances compound optimization processes.

Abstract

Drug addiction is a complex and pervasive global challenge that continues to pose significant public health concerns. Traditional approaches to anti-addiction drug discovery have struggled to deliver effective therapeutics, facing high attrition rates, long development timelines, and inefficiencies in processing large-scale data. Artificial intelligence (AI) has emerged as a transformative solution to address these issues. Using advanced algorithms, AI is revolutionizing drug discovery by enhancing the speed and precision of key processes. This review explores the transformative role of AI in the pipeline for anti-addiction drug discovery, including data collection, target identification, and compound optimization. By highlighting the potential of AI to overcome traditional barriers, this review systematically examines how AI addresses critical gaps in anti-addiction research, emphasizing its potential to revolutionize drug discovery and development, overcome challenges, and advance more effective therapeutic strategies.