Target-Specific Toxicity Knowledgebase (TsTKb): A Novel Toolkit for In Silico Predictive Technology
Document Type
Article
Publication Date
11-14-2018
Department
Computing
School
Computing Sciences and Computer Engineering
Abstract
As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.
Publication Title
Journal of Environmental Science and Health, Part C: Environmental Carcinogenesis and Ecotoxicology Reviews
Volume
36
Issue
4
First Page
219
Last Page
236
Recommended Citation
Li, Y.,
Idakwo, G.,
Thangapandian, S.,
Chen, M.,
Hong, H.,
Zhang, C.,
Gong, P.
(2018). Target-Specific Toxicity Knowledgebase (TsTKb): A Novel Toolkit for In Silico Predictive Technology. Journal of Environmental Science and Health, Part C: Environmental Carcinogenesis and Ecotoxicology Reviews, 36(4), 219-236.
Available at: https://aquila.usm.edu/fac_pubs/15724