Preprint Article Version 1 This version not peer reviewed

High-Performance Prediction of Agonists on Human Estrogen Receptor Based on Chemical Structures

Version 1 : Received: 16 March 2017 / Approved: 17 March 2017 / Online: 17 March 2017 (04:49:28 CET)

A peer-reviewed article of this Preprint also exists.

Asako, Y.; Uesawa, Y. High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures. Molecules 2017, 22, 675. Asako, Y.; Uesawa, Y. High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures. Molecules 2017, 22, 675.

Journal reference: Molecules 2017, 22, 675
DOI: 10.3390/molecules22040675

Abstract

Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4,000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. At this time, our model delivers the highest predictive power on estrogen receptor agonists in the world. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules.

Subject Areas

machine learning; random forest; estrogen receptor; Tox21 data challenge 2014; QSAR prediction model

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