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

Link: http://dx.doi.org/10.21079/11681/41302

This article was originally published online in Frontiers in Physiology (Systems Biology) on 13 August 2019. This work was supported by the U.S. Army Environmental Quality Research Program.

Report Number: ERDC/EL MP-21-3

Title: 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

By: Gabriel Idakwo, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang and Ping Gong

Approved for public release; distribution is unlimited.

July 2021

19 pages / 2.2 Mb