Quantitative Biology > Biomolecules
[Submitted on 5 Feb 2025
(v1)
, last revised 10 Feb 2025 (this version, v2)]
Title: Artificial Intelligence Approaches for Anti-Addiction Drug Discovery
Title: 抗成瘾药物发现的人工智能方法
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.
Submission history
From: Dong Chen [view email][v1] Wed, 5 Feb 2025 20:49:02 UTC (3,896 KB)
[v2] Mon, 10 Feb 2025 18:47:02 UTC (3,878 KB)
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