拓增强机器学习模型 (Top-ML) 用于抗癌预测
Joshua Zhi En Tan1, JunJie Wee2, Xue Gong1
1Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore.
Journal of chemical information and modeling
|April 14, 2025
概括
我们开发了一种新的拓增强机器学习 (Top-ML) 模型,用于预测抗癌. 这种方法使用独特的拓特征来改进人工智能驱动的药物发现,克服当前的特征化限制.
科学领域:
- 生物技术是生物技术.
- 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:
- 治疗性体在癌症治疗方面表现有前途.
- 人工智能 (AI) 有助于选抗癌.
- 高效的特征化是人工智能模型的一个瓶.
研究的目的:
- 为抗癌预测提出一个拓增强机器学习 (Top-ML) 模型.
- 解决AI模型中当前特征化方法的局限性.
主要方法:
- 从序列连接信息中使用类拓特征开发了Top-ML模型.
- 利用光谱描述器来表征拓.
- 为了预测,使用了"树外分类器".
主要成果:
- 在AntiCP 2.0和mACPpred 2.0数据集上验证了Top-ML.
- 实现了最先进的状态或与深度学习模型可比的性能.
- 与现有方法相比,证明了更大的解释性.
结论:
- 基于新型拓学的特征化加速了抗癌的识别.
- 顶级ML模型显示了人工智能驱动的癌症治疗的巨大潜力.
- 该方法提高了机器学习在药物发现中的效率和可解释性.
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