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Machine Learning-Based Modeling of Drug Toxicity

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Computational Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1754))

Abstract

Toxicity is an important reason for the failure of drug research and development (R&D). The traditional experimental testings for chemical toxicity profile are costly and time-consuming. Therefore, it is attractive to develop the effective and accurate alternatives, such as in silico prediction models. In this review, we discuss the practical use of some prediction models on three toxicity end points, including acute toxicity, carcinogenicity, and inhibition of the human ether-a-go-go-related gene ion channel (hERG). Special emphasis is put on the machine learning methods for developing in silico models, and their advantages and weaknesses are discussed. We conclude that machine learning methods are valuable for helping the process of designing new candidates with low toxicity in drug R&D studies. In the future, much still needs to be done to understand more completely the biological mechanisms for toxicity and to develop more accurate prediction models to screen compounds.

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Authors and Affiliations

  1. School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, China

    Jing Lu

  2. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

    Dong Lu, Zunyun Fu, Mingyue Zheng & Xiaomin Luo

  3. University of Chinese Academy of Sciences, Beijing, China

    Dong Lu

Authors
  1. Jing Lu
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  2. Dong Lu
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  3. Zunyun Fu
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  4. Mingyue Zheng
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  5. Xiaomin Luo
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Editors and Affiliations

  1. Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Tao Huang

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Lu, J., Lu, D., Fu, Z., Zheng, M., Luo, X. (2018). Machine Learning-Based Modeling of Drug Toxicity. In: Huang, T. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 1754. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7717-8_15

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  • DOI: https://doi.org/10.1007/978-1-4939-7717-8_15

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7716-1

  • Online ISBN: 978-1-4939-7717-8

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