The applications of machine learning algorithms in the modeling of estrogen-like chemicals

Huanxiang Liu, Xiaojun Yao, Paola Gramatica

Research output: Contribution to journalReview articlepeer-review

17 Citations (Scopus)


Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that, in the environment, are adversely affecting human and wildlife health through a variety of mechanisms, mainly estrogen receptor-mediated mechanisms of toxicity. Because of the large number of such chemicals in the environment, there is a great need for an effective means of rapidly assessing endocrine-disrupting activity in the toxicology assessment process. When faced with the challenging task of screening large libraries of molecules for biological activity, the benefits of computational predictive models based on quantitative structure-activity relationships to identify possible estrogens become immediately obvious. Recently, in order to improve the accuracy of prediction, some machine learning techniques were introduced to build more effective predictive models. In this review we will focus our attention on some recent advances in the use of these methods in modeling estrogen-like chemicals. The advantages and disadvantages of the machine learning algorithms used in solving this problem, the importance of the validation and performance assessment of the built models as well as their applicability domains will be discussed.

Original languageEnglish
Pages (from-to)490-496
Number of pages7
JournalCombinatorial Chemistry and High Throughput Screening
Issue number5
Publication statusPublished - Jun 2009
Externally publishedYes


  • Artificial neural networks
  • Classification
  • Estrogen-like chemicals
  • Molecular descriptors
  • Quantitative structure-activity relationships
  • Regression
  • Support vector machines
  • Validation


Dive into the research topics of 'The applications of machine learning algorithms in the modeling of estrogen-like chemicals'. Together they form a unique fingerprint.

Cite this