Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Androgen Receptor Binding Prediction with Random Forest, Deep Neural Networks, and Graph Convolutional Neural Networks

Version 1 : Received: 15 February 2021 / Approved: 16 February 2021 / Online: 16 February 2021 (10:04:48 CET)
Version 2 : Received: 18 February 2021 / Approved: 18 February 2021 / Online: 18 February 2021 (16:05:17 CET)
Version 3 : Received: 23 February 2021 / Approved: 24 February 2021 / Online: 24 February 2021 (13:14:01 CET)

A peer-reviewed article of this Preprint also exists.

García-Sosa, A.T. Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features. Molecules 2021, 26, 1285. García-Sosa, A.T. Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features. Molecules 2021, 26, 1285.

Journal reference: Molecules 2021, 26, 1285
DOI: 10.3390/molecules26051285

Abstract

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 evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to different results, with deep neural networks on user-defined physicochemically-relevant features developed for this work outperform graph convolutional, random forest, and large featurizations. The results can help provide clues on risk of substances and better experimental design for toxicity assays. Source code and data are available at https://github.com/AlfonsoTGarcia-Sosa/ML

Subject Areas

Machine Learning; Artificial Intelligence; Androgen Receptor; Random Forest; Deep Neural Network; Convolutional

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.