Abstract
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.
Keywords: Estrogen-like chemicals, quantitative structure-activity relationships, classification, regression, molecular descriptors, validation, support vector machines, artificial neural networks
Combinatorial Chemistry & High Throughput Screening
Title: The Applications of Machine Learning Algorithms in the Modeling of Estrogen-Like Chemicals
Volume: 12 Issue: 5
Author(s): Huanxiang Liu, Xiaojun Yao and Paola Gramatica
Affiliation:
Keywords: Estrogen-like chemicals, quantitative structure-activity relationships, classification, regression, molecular descriptors, validation, support vector machines, artificial neural networks
Abstract: 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.
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Cite this article as:
Liu Huanxiang, Yao Xiaojun and Gramatica Paola, The Applications of Machine Learning Algorithms in the Modeling of Estrogen-Like Chemicals, Combinatorial Chemistry & High Throughput Screening 2009; 12 (5) . https://dx.doi.org/10.2174/138620709788489037
DOI https://dx.doi.org/10.2174/138620709788489037 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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