机器学习方法用于预测全位
Francho Nerín-Fonz1, Zoe Cournia1
1Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece; Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
Current opinion in structural biology
|February 14, 2024
概括
本综述涵盖了用于预测全位的机器学习模型,这对于药物发现至关重要. 未来的AI进步,包括蛋白质语言模型,有望增强全调节器的开发.
科学领域:
- 生物化学和分子生物学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 体调节通过调节器控制细胞过程,通过调节器与远端蛋白位点结合.
- 阿洛斯特基调节器比奥托斯特基调节器具有优势,推动了计算药物发现工作.
- 识别全位是开发新疗法的关键.
研究的目的:
- 审查现有的机器学习 (ML) 模型,用于预测全位.
- 讨论人工智能 (AI) 在推进全药物发现方面的潜力.
- 突出未来的观点,包括蛋白质语言模型的作用.
主要方法:
- 对计算方法的文献综述,以识别全位点.
- 机器学习模型应用在预测全结合部位方面的分析.
- 探索新兴的人工智能技术,与全调制相关.
主要成果:
- 已经开发了几种ML模型来预测全位.
- 这些模型利用计算策略来识别潜在的药物目标.
- 审查综合了当前的方法及其成功.
结论:
- 机器学习模型是预测全位的宝贵工具.
- 人工智能,特别是蛋白质语言模型,提供了有希望的未来方向.
- 计算方法的进步将加速全性药物发现.
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