scholarly journals Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay Data

2019 ◽  
Vol 10 ◽  
Author(s):  
Gabriel Idakwo ◽  
Sundar Thangapandian ◽  
Joseph Luttrell ◽  
Zhaoxian Zhou ◽  
Chaoyang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Androgen Receptor ◽  
High Throughput ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Structure Activity ◽  
Bioassay Data

scholarly journals Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data

2021 ◽  
Author(s):  
Gabriel Idakwo ◽  
Sundar Thangapandian ◽  
Joseph Luttrell ◽  
Zhaoxian Zhou ◽  
Chaoyang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Androgen Receptor ◽  
Learning Algorithms ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Chemical Toxicity ◽  
Toxicity Prediction ◽  
Predictive Toxicology ◽  
Structure Activity

Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.

Oxidative folding and preparation of α-conotoxins for use in high-throughput structure-activity relationship studies

2012 ◽  
Vol 19 (1) ◽  
pp. 16-24 ◽  
Author(s):  
Reena Gyanda ◽  
Jayati Banerjee ◽  
Yi-Pin Chang ◽  
Angela M. Phillips ◽  
Lawrence Toll ◽  
...  
Keyword(s):  
High Throughput ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Oxidative Folding ◽  
Structure Activity

High-Throughput Chemical Modification of Oligonucleotides for Systematic Structure–Activity Relationship Evaluation

2014 ◽  
Vol 25 (12) ◽  
pp. 2222-2232 ◽  
Author(s):  
Daniel Zewge ◽  
Francis Gosselin ◽  
Denise M. Kenski ◽  
Jenny Li ◽  
Vasant Jadhav ◽  
...  
Keyword(s):  
Chemical Modification ◽  
High Throughput ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Structure Activity ◽  
Systematic Structure
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