机器学习用于多目标药物发现:系统药理学的挑战和机遇
Xueyuan Bi1, Yangyang Wang2, Jihan Wang3
1Department of Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an 710054, China.
机器学习 (ML) 通过分析生物数据,加速针对复杂疾病的多目标药物发现. 本综述探讨了ML的应用,挑战和开发精密多药学的未来方向.
科学领域:
- 药理学和计算生物学
- 人工智能在药物发现中的作用
背景情况:
- 复杂的疾病需要多目标药物发现,因为单个目标方法是不够的.
- 机器学习 (ML) 提供了强大的工具来优化多目标药物开发,利用大型生物数据集和算法进步.
研究的目的:
- 为应用在多目标药物发现中的ML技术提供全面的概述.
- 突出该领域的应用,挑战和未来方向.
主要方法:
- 审查各种ML技术,包括深度学习 (DL),基于注意力的模型,基于图形和多任务学习框架.
- 分析瘤学,中枢神经系统疾病和药物重用中的现实应用.
主要成果:
- 机器学习有效地帮助多目标预测和复杂疾病的药物开发.
- 确定的主要挑战包括数据稀疏性,可解释性,可概括性和整合到实验工作流中.
结论:
- ML对于推进精密多药理学和开发更安全,更有效的多目标疗法至关重要.
- 未来的方向包括生成建模,联合学习和个性化医学的患者特异性治疗设计.
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