2-D chemical structure image-based in silico model to predict agonist activity for androgen receptor

BMC Bioinformatics

Jingyu Lee¹  Yongmin Lee¹  Myeong-Sang Yu¹  Dokyun Na¹ 

[1] School of Integrative Engineering, Department of Biomedical Engineering, Chung-Ang University, 06974, Seoul, Republic of Korea;

关键词: Chemical compound images;    Convolutional neural network;    Androgen receptor toxicity;   

DOI  :  10.1186/s12859-020-03588-1

来源: Springer

【 摘 要 】

BackgroundAbnormal 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.ResultIn 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.ConclusionOur 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.

【 授权许可】

CC BY