深度学习促进类药物开发:结构预测和设计中的工具和方法
Xinyi Wu1, Huitian Lin1, Renren Bai2
1College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China.
European journal of medicinal chemistry
|February 22, 2024
概括
深度学习增强了对具有挑战性的疾病的类药物设计. 本综述详细介绍了使用先进的计算方法开发疗法的方法和未来方向.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 类提供高亲和力和特异性疾病目标,解决未满足的医疗需求.
- 与蛋白质相比,的设计面临着挑战,因为它们的尺寸较小,灵活性较大,数据有限.
- 深度学习的进步为克服这些设计障碍提供了机会.
研究的目的:
- 审查深度学习在治疗中的应用.
- 利用人工智能探索增强结构预测和设计的方法.
- 引导研究人员利用深度学习来开发类药物.
主要方法:
- 数据集的策划和处理用于类研究.
- 开发和应用深度学习模型用于结构预测.
- 在人工智能驱动的设计中整合结构-活动关系原则.
主要成果:
- 深度学习模型在提高结构预测准确度方面表现有前途.
- 由人工智能驱动的方法可以加速新疗法的设计.
- 该审查综合了该领域当前的挑战和未来的机遇.
结论:
- 深度学习是类疗法的转型技术.
- 人工智能模型的持续改进将推动类药物发现.
- 解决数据局限性和模型可解释性是未来的关键方向.
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