Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

• Main Authors: Yunyi Wu, Guanyu Wang
• Format: Article
• Language: English
• Published: MDPI AG 2018-08-01
• Series: International Journal of Molecular Sciences
• Subjects: toxicity prediction; machine learning; deep learning; transcriptome; chemical structure; molecular fingerprint; molecular fragment
• Online Access: http://www.mdpi.com/1422-0067/19/8/2358

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.