抗成瘾药物发现的人工智能方法。

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Dong Chen, Jian Jiang, Nicole Hayes, Zhe Su, Guo-Wei Wei
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引用次数: 0

摘要

药物成瘾仍然是一个复杂的全球公共卫生挑战,传统的抗成瘾药物的发现受到疗效有限和针对复杂神经化学系统进展缓慢的阻碍。人工智能(AI)中的先进算法提供了一种变革性的解决方案,可以提高治疗开发的速度和精度。这篇综述探讨了人工智能如何通过靶向阿片系统以及多巴胺和gaba能系统来开发抗成瘾药物的关键因素,这些系统在成瘾病理中是必不可少的。它通过创新的通用药物发现技术,确定了在研究较少研究的成瘾相关系统方面即将出现的趋势。人工智能有可能打破传统的限制,改变反成瘾研究,从而开发出更好的治疗方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence approaches for anti-addiction drug discovery.

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.

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CiteScore
2.80
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