Computational Toxicology

Chapter 11Big Data in Computational Toxicology: Challenges and Opportunities

Linlin Zhao¹ and Hao Zhu^1,2

¹Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA

²Department of Chemistry, Rutgers University, Camden, NJ, USA

Chapter Menu

1. Big Data Scenario of Computational Toxicology
2. Fast-Growing Chemical Toxicity Data
3. The Use of Big Data Approaches in Modern Computational Toxicology
4. Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts

11.1 Big Data Scenario of Computational Toxicology

In the past decade, along with the vibrant and rapid development of chemical synthesis and biological screening technologies, immense data were generated daily and most of these data are available to the public [1, 2]. Biological data generated from high-throughput screening (HTS) of large chemical libraries contains rich toxicology information that has the potential to be integrated into toxicity research [3]. Currently available toxicity data exist both in structured formats (e.g., deposited into PubChem and other data-sharing web portals) and as unstructured data (papers, laboratory reports, toxicity web-site information, etc.). These data, even in structured formats, become so large and complex that it is difficult to process and use them with traditional database management, data processing, and modeling tools.

Figure 11.1 The ...