Machine Learning-Based Modeling of Drug Toxicity

  • Jing Lu
  • Dong Lu
  • Zunyun Fu
  • Mingyue Zheng
  • Xiaomin Luo
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)


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.

Key words

Machine learning method In silico model Acute toxicity Carcinogenicity hERG 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jing Lu
    • 1
  • Dong Lu
    • 2
    • 3
  • Zunyun Fu
    • 2
  • Mingyue Zheng
    • 2
  • Xiaomin Luo
    • 2
  1. 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 ShandongYantai UniversityYantaiChina
  2. 2.Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia MedicaChinese Academy of SciencesShanghaiChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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