Department of Biology, Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research, Southern University of Science and Technology, Shenzhen 518055, China.
Int J Mol Sci. 2018 Aug 10;19(8):2358. doi: 10.3390/ijms19082358.
Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.
毒性预测对公共卫生非常重要。在其众多应用中，毒性预测对于降低药物临床前和临床试验的成本和劳动力至关重要，因为由于预测的毒性，可以避免大量的药物评估（细胞、动物和临床）。在大数据和人工智能时代，毒性预测可以受益于机器学习，它已经在自然语言处理、语音识别、图像识别、计算化学和生物信息学等许多领域得到了广泛应用，并且具有出色的性能。在本文中，我们回顾了已经应用于毒性预测的机器学习方法，包括深度学习、随机森林、k-最近邻和支持向量机。我们还讨论了机器学习算法的输入参数，特别是它从仅化学结构描述到与人类转录组数据分析相结合的转变，这可以极大地提高预测准确性。
