Chen Dong, Jiang Jian, Hayes Nicole, Su Zhe, Wei Guo-Wei
Department of Mathematics, Michigan State University MI 48824 USA
Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University Wuhan 430200 P. R. China.
Digit Discov. 2025 May 13. doi: 10.1039/d5dd00032g.
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.
药物成瘾仍然是一项复杂的全球公共卫生挑战,传统的抗成瘾药物研发因疗效有限以及在针对复杂神经化学系统方面进展缓慢而受阻。人工智能(AI)中的先进算法提供了一种变革性的解决方案,可提高治疗开发的速度和精度。本文综述探讨了人工智能如何通过靶向阿片系统以及多巴胺能和GABA能系统,在抗成瘾药物开发中发挥关键作用,这些系统在成瘾病理学中至关重要。它确定了通过创新的通用药物发现技术,在研究较少研究的成瘾相关系统方面有前景的未来趋势。人工智能有潜力通过打破传统限制来改变抗成瘾研究,这将有助于开发出更优的治疗方法。