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Article

Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features

Institute of Chemistry, University of Tartu, Ravila 14a, 54011 Tartu, Estonia
Academic Editors: Jay McLaughlin, Fernanda Borges, Sofia Benfeito, Daniel Chavarria and Pedro Soares
Molecules 2021, 26(5), 1285; https://doi.org/10.3390/molecules26051285
Received: 15 January 2021 / Revised: 23 February 2021 / Accepted: 24 February 2021 / Published: 26 February 2021
(This article belongs to the Special Issue Recent Advances in CNS Drug Discovery)
Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain development, and prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, and rat effects by chemicals have been used to build machine-learning classifiers and regressors and to evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to different results, with deep neural networks (DNNs) on user-defined physicochemically relevant features developed for this work outperforming graph convolutional, random forest, and large featurizations. The results show that these user-provided structure-, ligand-, and statistically based features and specific DNNs provided the best results as determined by AUC (0.87), MCC (0.47), and other metrics and by their interpretability and chemical meaning of the descriptors/features. In addition, the same features in the DNN method performed better than in a multivariate logistic model: validation MCC = 0.468 and training MCC = 0.868 for the present work compared to evaluation set MCC = 0.2036 and training set MCC = 0.5364 for the multivariate logistic regression on the full, unbalanced set. Techniques of this type may improve AR and toxicity description and prediction, improving assessment and design of compounds. Source code and data are available on github. View Full-Text
Keywords: machine learning; artificial intelligence; androgen receptor; random forest; deep neural network machine learning; artificial intelligence; androgen receptor; random forest; deep neural network
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MDPI and ACS Style

García-Sosa, A.T. Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features. Molecules 2021, 26, 1285. https://doi.org/10.3390/molecules26051285

AMA Style

García-Sosa AT. Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features. Molecules. 2021; 26(5):1285. https://doi.org/10.3390/molecules26051285

Chicago/Turabian Style

García-Sosa, Alfonso T. 2021. "Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features" Molecules 26, no. 5: 1285. https://doi.org/10.3390/molecules26051285

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