2-D chemical structure image-based in silico model to predict agonist activity for androgen receptor
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RESEARCH
Open Access
2-D chemical structure image-based in silico model to predict agonist activity for androgen receptor Myeong-Sang Yu, Jingyu Lee, Yongmin Lee and Dokyun Na* From The 13th International Workshop on Data and Text Mining in Biomedical Informatics Beijing, China. 3-7 November 2019
* Correspondence: [email protected]. kr School of Integrative Engineering, Department of Biomedical Engineering, Chung-Ang University, Seoul, Republic of Korea 06974
Abstract Background: Abnormal activation of human nuclear hormone receptors disrupts endocrine systems and thereby affects human health. There have been machine learning-based models to predict androgen receptor agonist activity. However, the models were constructed based on limited numerical features such as molecular descriptors and fingerprints. Result: In this study, instead of the numerical features, 2-D chemical structure images of compounds were used to build an androgen receptor toxicity prediction model. The images may provide unknown features that were not represented by conventional numerical features. As a result, the new strategy resulted in a construction of highly accurate prediction model: Mathews correlation coefficient (MCC) of 0.688, positive predictive value (PPV) of 0.933, sensitivity of 0.519, specificity of 0.998, and overall accuracy of 0.981 in 10-fold cross-validation. Validation on a test dataset showed MCC of 0.370, sensitivity of 0.211, specificity of 0.991, PPV of 0.882, and overall accuracy of 0.801. Our chemical image-based prediction model outperforms conventional models based on numerical features. Conclusion: Our constructed prediction model successfully classified molecular images into androgen receptor agonists or inactive compounds. The result indicates that 2-D molecular mimetic diagram would be used as another feature to construct molecular activity prediction models. Keywords: Chemical compound images, Convolutional neural network, Androgen receptor toxicity
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