Background: Cancer is the second leading cause of human death in the world. To date, many factors have been confirmed to be the cause of cancer. Among them, carcinogenic chemicals have been widely accepted as the important ones. Traditional methods for detecting carcinogenic chemicals are of low efficiency and high cost.

Objective: The aim of this study was to design an efficient computational method for the identification of carcinogenic chemicals.

Methods: A new computational model was proposed for detecting carcinogenic chemicals. As a data-driven model, carcinogenic and non-carcinogenic chemicals were obtained from Carcinogenic Potency Database (CPDB). These chemicals were represented by features extracted from five chemical networks, representing five types of chemical associations, via a network embedding method, Mashup. Obtained features were fed into a powerful deep learning method, recurrent neural network, to build the model.

Results: The jackknife test on such model provided the F-measure of 0.971 and AUROC of 0.971.

Conclusion: The proposed model was quite effective and was superior to the models with traditional machine learning algorithms, classic chemical encoding schemes or direct usage of chemical associations.

背景：癌症是世界上第二大人类死亡的主要原因。迄今为止，已经证实许多因素是癌症的原因。其中，致癌化学物质已被广泛认为是重要的化学物质。用于检测致癌化学物质的传统方法效率低且成本高。

目的：本研究的目的是设计一种用于鉴定致癌化学物质的有效计算方法。

方法：提出了一种新的检测致癌化学物质的计算模型。作为数据驱动的模型，从致癌潜能数据库（CPDB）获得了致癌和非致癌化学物质。通过网络嵌入方法Mashup，从五个化学网络（代表五种化学缔合）提取的特征表示这些化学物质。将获得的特征输入到功能强大的深度学习方法（递归神经网络）中，以构建模型。

结果：在该模型上进行的折刀测试提供了0.971的F值和0.971的AUROC。

结论：所提出的模型非常有效，并且优于具有传统机器学习算法，经典化学编码方案或直接使用化学缔合的模型。