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MLASM:基于机器学习的抗癌小分子预测

Priya Dharshini Balaji1, Subathra Selvam1, Honglae Sohn2

  • 1Computational Biology Laboratory, Department of Genetic Engineering, School of Bio-Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India.

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概括

机器学习模型被开发用于预测抗癌小分子 (ACSMs),克服实验识别的局限性. 轻GBM模型表现出卓越的预测性能,达到79%的准确性和0.88.8的AUC.

关键词:
抗癌是一种抗癌.抗癌小分子 抗癌小分子灯光梯度增强机器. 灯光梯度增强机器. 随机的森林随机的森林机器学习是机器学习.

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科学领域:

  • 计算化学和生物信息学
  • 药物的发现和开发.

背景情况:

  • 癌症是全球主要的死亡原因,需要有效的治疗.
  • 抗癌 (ACP) 显示出治疗前景,但实验性鉴定是昂贵和耗时的.
  • 抗癌小分子 (ACSMs) 提供了一个替代的治疗策略.

研究的目的:

  • 开发一种机器学习 (ML) 模型,用于预测抗癌小分子 (ACSM).
  • 克服实验识别潜在抗癌剂的局限性.
  • 评估各种ML算法在预测ACSM中的性能.

主要方法:

  • 开发了使用五个算法的ML模型:随机森林 (RF),LightGBM,K-最近邻居 (KNN),决策树 (DT) 和极端梯度增强 (XGB).
  • 在一万个化合物的数据集上训练模型.
  • 使用测试组和外部验证与FDA批准的抗癌药物验证模型性能.

主要成果:

  • 确定了RF,LightGBM和XGB作为前三大表现最好的模型.
  • 轻GBM模型实现了最高的精度 (79%),曲线下的面积 (AUC) 为0.88.
  • 在外部验证中,LightGBM正确预测了10种活性化合物中的9种,表现优于RF (8/10) 和XGB (7/10).

结论:

  • 开发的ML模型,特别是LightGBM,显示出强大的预测能力,用于识别抗癌小分子.
  • 这种基于机器学习的方法为药物发现的实验方法提供了更有效和更具成本效益的替代方案.
  • 机器学习对推进癌症治疗研究和加速开发新型抗癌疗法具有重大潜力.