艾洛斯特里发光:利用人工智能和机器学习来发现药物
Maria-Jesus Blanco1, Melissa J Buskes1, Rajiv G Govindaraj1
1Atavistik Bio, 101 Cambridgepark Drive, Cambridge, Massachusetts 02140, United States.
ACS medicinal chemistry letters
|September 18, 2024
概括
人工智能 (AI) 和机器学习 (ML) 加快了药物发现,特别是对于全调节器. 在AlphaFold和基于结构的药物发现的突破正在推动这一领域的发展,尽管仍然存在挑战.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在制药研究中越来越多地被采用.
- 艾洛斯特基调节器具有治疗潜力,但也带来了发现挑战.
研究的目的:
- 审查最近的AI/ML应用在全调节器发现中的应用.
- 为突出AlphaFold,基于结构的药物发现 (SBDD) 和药物化学的进步.
- 讨论目前在识别全位和连接体方面的挑战.
主要方法:
- 关于AI/ML在药物发现中的最新文献的综述.
- 专注于AlphaFold用于蛋白质结构预测.
- 对SBDD和药物化学应用的分析.
主要成果:
- 包括AlphaFold在内的AI/ML工具正在显著影响全调节器的发现.
- 基于结构的方法与AI/ML相结合,显示出有希望的结果.
- 在将计算方法集成到药物设计中取得了进展.
结论:
- 人工智能和机器学习是革命性的强大工具,彻底改变了全调节器药物发现.
- 需要进一步的进展,以克服全位和连接体识别方面的挑战.
- 整合AI/ML有望实现更高效,更有效的药物开发管道.
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