解决基于人工智能的药物发现数据短缺问题的当前策略:全面审查
Amit Gangwal1, Azim Ansari2, Iqrar Ahmad3
1Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule, 424001, Maharashtra, India.
Computers in biology and medicine
|July 4, 2024
概括
药物发现中的人工智能 (AI) 面临着数据限制. 本综述将转移学习和联合学习等方法进行比较,以在有限的数据中提高AI模型性能.
科学领域:
- 计算化学和化学信息学
- 机器学习和人工智能在药物发现中的作用
- 药物化学和药物设计.
背景情况:
- 人工智能 (AI) 显著影响计算机辅助药物设计 (CADD),由机器学习 (ML),深度学习 (DL) 和先进计算驱动.
- 尽管人工智能在药物发现和药物化学方面的好处很大,但它的应用面临着一些挑战,包括数据不足,不统一,没有标签,以及知识产权问题.
- 人工智能在药物发现中的成功在很大程度上取决于培训数据的质量和数量,数据饥渴的深度学习模型特别容易受到数据稀缺的影响.
研究的目的:
- 审查和比较各种方法来解决AI驱动药物发现中的数据限制.
- 讨论转移学习,主动学习和联合学习等技术在建模小分子数据中的应用和局限性.
- 探索处理不充分数据的新方法,以提高药物设计中的AI模型性能.
主要方法:
- 对人工智能中用于药物发现的数据处理技术的现有文献进行比较分析.
- 讨论包括转移学习 (TL),主动学习 (AL),单次学习 (OSL),多任务学习 (MTL),数据增强 (DA) 和数据合成 (DS) 在内的方法.
- 探索联合学习 (FL) 作为一种保护隐私的方法,用于在专有数据集上进行协作模式培训.
主要成果:
- 包括TL,AL,OSL,MTL,DA和DS在内的几种方法在改善AI模型输出方面表现出有效性,而药物发现的数据有限.
- 联合学习 (FL) 提供了一个独特的解决方案,用于在分布式,私有数据集上训练ML模型,而不会影响数据隐私.
- 该审查强调了数据质量和数量的关键作用,以及需要强大的方法来克服AI目前与数据相关的局限性.
结论:
- 解决数据稀缺性和质量问题对于实现AI在药物发现方面的全部潜力至关重要.
- 各种学习和数据增强策略可以有效地减轻有限数据对AI模型性能的影响.
- 联合学习为安全的数据共享和制药研究中的协作模型开发提供了一个有希望的途径.
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