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Int. J. Mol. Sci. 2018, 19(8), 2358; https://doi.org/10.3390/ijms19082358

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

Department of Biology, Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research, Southern University of Science and Technology, Shenzhen 518055, China
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Received: 30 June 2018 / Revised: 31 July 2018 / Accepted: 8 August 2018 / Published: 10 August 2018
(This article belongs to the Special Issue Frontiers in Drug Toxicity Prediction)
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Abstract

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. View Full-Text
Keywords: toxicity prediction; machine learning; deep learning; transcriptome; chemical structure; molecular fingerprint; molecular fragment toxicity prediction; machine learning; deep learning; transcriptome; chemical structure; molecular fingerprint; molecular fragment
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Wu, Y.; Wang, G. Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis. Int. J. Mol. Sci. 2018, 19, 2358.

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