Chapter 6
Mode-of-Action-Guided, Molecular
Modeling-Based Toxicity Prediction:
A Novel Approach for In Silico Predictive
Toxicology
Ping Gong, Sundar Thangapandian, Yan Li, Gabriel Idakwo,
Joseph Luttrell IV, Minjun Chen, Huixiao Hong and Chaoyang Zhang
Abstract Computational toxicology is a sub-discipline of toxicology concerned
with the development and use of computer-based models and methodology to understand and predict chemical toxicity in a biological system (e.g., cells and organisms). Quantitative structure–activity relationship (QSAR) has been the predominant approach in computational toxicology. However, classical QSAR methodology
has often suffered from low prediction accuracy, largely owing to the lack or nonintegration of toxicological mechanisms. To address this lingering problem, we have
P. Gong (B) · S. Thangapandian
Environmental Laboratory, US Army Engineer Research and Development Center,
Vicksburg, MS 39180, USA
e-mail: Ping.Gong@usace.army.mil
S. Thangapandian
e-mail: Sundarapandian.Thangapandian@usace.army.mil
Y. Li
Bennett Aerospace, Inc., Cary, NC 27518, USA
e-mail: yli@bennettaerospace.com
G. Idakwo · J. Luttrell IV · C. Zhang
School of Computing Sciences and Computer Engineering,
University of Southern Mississippi, Hattiesburg, MS 39406, USA
e-mail: Gabriel.Idakwo@usm.edu
J. Luttrell IV
e-mail: Joseph.Luttrell@usm.edu
C. Zhang
e-mail: Chaoyang.Zhang@usm.edu
M. Chen · H. Hong
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research,
U.S. Food and Drug Administration, Jefferson, AR 72079, USA
e-mail: Minjun.Chen@fda.hhs.gov
H. Hong
e-mail: Huixiao.Hong@fda.hhs.gov
© This is a U.S. government work and not under copyright protection in the U.S.; foreign
copyright protection may apply 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_6
99
Mode-of-Action-Guided, Molecular
Modeling-Based Toxicity Prediction:
A Novel Approach for In Silico Predictive
Toxicology
Ping Gong, Sundar Thangapandian, Yan Li, Gabriel Idakwo,
Joseph Luttrell IV, Minjun Chen, Huixiao Hong and Chaoyang Zhang
Abstract Computational toxicology is a sub-discipline of toxicology concerned
with the development and use of computer-based models and methodology to understand and predict chemical toxicity in a biological system (e.g., cells and organisms). Quantitative structure–activity relationship (QSAR) has been the predominant approach in computational toxicology. However, classical QSAR methodology
has often suffered from low prediction accuracy, largely owing to the lack or nonintegration of toxicological mechanisms. To address this lingering problem, we have
P. Gong (B) · S. Thangapandian
Environmental Laboratory, US Army Engineer Research and Development Center,
Vicksburg, MS 39180, USA
e-mail: Ping.Gong@usace.army.mil
S. Thangapandian
e-mail: Sundarapandian.Thangapandian@usace.army.mil
Y. Li
Bennett Aerospace, Inc., Cary, NC 27518, USA
e-mail: yli@bennettaerospace.com
G. Idakwo · J. Luttrell IV · C. Zhang
School of Computing Sciences and Computer Engineering,
University of Southern Mississippi, Hattiesburg, MS 39406, USA
e-mail: Gabriel.Idakwo@usm.edu
J. Luttrell IV
e-mail: Joseph.Luttrell@usm.edu
C. Zhang
e-mail: Chaoyang.Zhang@usm.edu
M. Chen · H. Hong
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research,
U.S. Food and Drug Administration, Jefferson, AR 72079, USA
e-mail: Minjun.Chen@fda.hhs.gov
H. Hong
e-mail: Huixiao.Hong@fda.hhs.gov
© This is a U.S. government work and not under copyright protection in the U.S.; foreign
copyright protection may apply 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_6
99