Guo Wenjing, Liu Jie, Dong Fan, Hong Huixiao
National Center for Toxicological Research (NCTR), U.S. Food & Drug Administration (FDA), Jefferson, AR.
J Environ Sci Health C Toxicol Carcinog. 2025;43(1):23-50. doi: 10.1080/26896583.2024.2396731. Epub 2024 Sep 3.
The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effective, suffer from being costly and time-consuming. In response to this challenge, new alternative methodologies, notably machine learning and deep learning techniques, have attracted attention for their potential in developing carcinogenicity prediction models. This article reviews the progress in predicting carcinogenicity using various machine learning and deep learning algorithms. A comparative analysis on these developed models reveals that support vector machine, random forest, and ensemble learning are commonly preferred for their robustness and effectiveness in predicting chemical carcinogenicity. Conversely, models based on deep learning algorithms, such as feedforward neural network, convolutional neural network, graph convolutional neural network, capsule neural network, and hybrid neural networks, exhibit promising capabilities but are limited by the size of available carcinogenicity datasets. This review provides a comprehensive analysis of current machine learning and deep learning models for carcinogenicity prediction, underscoring the importance of high-quality and large datasets. These observations are anticipated to catalyze future advancements in developing effective and generalizable machine learning and deep learning models for predicting chemical carcinogenicity.
围绕化学物质致癌潜力的担忧不断升级，凸显了对高效致癌性评估方法的迫切需求。传统的实验方法，如体外和体内试验，虽然有效，但成本高昂且耗时。为应对这一挑战，新的替代方法，特别是机器学习和深度学习技术，因其在开发致癌性预测模型方面的潜力而受到关注。本文综述了使用各种机器学习和深度学习算法预测致癌性的进展。对这些已开发模型的比较分析表明，支持向量机、随机森林和集成学习因其在预测化学物质致癌性方面的稳健性和有效性而通常受到青睐。相反，基于深度学习算法的模型，如前馈神经网络、卷积神经网络、图卷积神经网络、胶囊神经网络和混合神经网络，展现出了有前景的能力，但受到可用致癌性数据集规模的限制。本综述对当前用于致癌性预测的机器学习和深度学习模型进行了全面分析，强调了高质量和大规模数据集的重要性。预计这些观察结果将推动未来在开发有效且通用的机器学习和深度学习模型以预测化学物质致癌性方面取得进展。
