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PECAN使用深度学习预测了自然产品癌细胞细胞静态活动的模式

Martha Gahl¹, Hyun Woo Kim^2,3, Evgenia Glukhov²

¹Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, United States.

Journal of natural products

|February 13, 2024

概括

我们开发了PECAN,这是一种预测抗癌药物活性的机器学习工具. 它以高精度对59个癌症细胞系的复合抗增殖作用进行了分类.

科学领域:

计算化学是一种计算化学.
机器学习在药物发现中的作用
在瘤学瘤学.

背景情况:

机器学习通过将化合物结构与生物功能联系起来,加速药物发现.
当前的模型通常为有限的药物标提供二元活动预测,特别是在抗癌研究中.
需要更细微的方法来预测不同类型癌症的药物疗效.

研究的目的:

引入PECAN (自然产品类化合物的细胞静态活性预测引擎),一个新的前神经网络.
为了能够同时对抗59个癌症细胞系的化合物进行抗增殖活性的分类.
不仅可以预测生物活动的存在,还可以预测生物活动的程度.

主要方法:

开发一个前神经网络架构.
使用国家癌症研究所 (NCI) 数据进行培训和验证.
对分类准确性的评估和复合结构特征的分析.

主要成果:

在一个六路分类任务中,PECAN实现了60.1%的准确性.
该模型表现出高性能,测量精度为93.0%的"一以内"测量精度.
有证据表明,PECAN利用有意义的结构特征进行预测.

结论:

在预测抗癌药物活性方面,PECAN提供了显著的进展.
该模型提供分级活动预测的能力提高了其在药物发现中的实用性.
PECAN依赖于结构特征表明其在识别新疗法化合物的潜力.

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