Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 19, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/ijms19082358
Keywords
toxicity prediction; machine learning; deep learning; transcriptome; chemical structure; molecular fingerprint; molecular fragment
Funding
- National Natural Science Foundation of China [61773196, 61471186]
- Shenzhen Municipal Research Fund [JCYJ20170307104535585, JCYJ20170817104740861]
- Shenzhen Peacock Plan [KQTD2016053117035204]
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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.
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