Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction.

ABSTRACT: Many chemicals that disrupt endocrine function have been linked to a variety of adverse biological outcomes. However, screening for endocrine disruption using in vitro or in vivo approaches is costly and time-consuming. Computational methods, e.g., quantitative structure-activity relationship models, have become more reliable due to bigger training sets, increased computing power, and advanced machine learning algorithms, such as multilayered artificial neural networks. Machine learning models can be used to predict compounds for endocrine disrupting capabilities, such as binding to the estrogen receptor (ER), and allow for prioritization and further testing. In this work, an exhaustive comparison of multiple machine learning algorithms, chemical spaces, and evaluation metrics for ER bindi

SUBMITTER: Russo DP 

PROVIDER: S-EPMC6181119 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature