Medicine and Health Sciences Commons^™

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, Gabriel Idakwo, Sundar Thangapandian, Joseph Luttrell Iv, Zhaoxian Zhou, Chaoyang Zhang, Ping Gong

Faculty Publications

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 …