范围(计算机科学)
天然产物
癌细胞系
药物发现
功能(生物学)
集合(抽象数据类型)
人工智能
计算生物学
计算机科学
人工神经网络
抗癌药
生物活性
数量结构-活动关系
机器学习
癌症
药品
生物
生物信息学
癌细胞
药理学
体外
生物化学
遗传学
进化生物学
程序设计语言
作者
Martha Gahl,Hyun Woo Kim,Evgenia Glukhov,William H. Gerwick,Garrison W. Cottrell
标识
DOI:10.1021/acs.jnatprod.3c00879
摘要
Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines. It predicts the activity to be one of six categories, indicating not only if activity is present but the degree of activity. Using an independent subset of NCI data as a test set, we show that PECAN can reach 60.1% accuracy in a six-way classification and present further evidence that it classifies based on useful structural features of compounds using a "within-one" measure that reaches 93.0% accuracy.
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