Browse > Article
http://dx.doi.org/10.9713/kcer.2019.57.6.781

Smart Synthetic Path Search System for Prevention of Hazardous Chemical Accidents and Analysis of Reaction Risk  

Jeong, Joonsoo (Department of Chemical Engineering, Myongji University)
Kim, Chang Won (Department of Chemical Engineering, Myongji University)
Kwak, Dongho (Department of Chemical Engineering, Myongji University)
Shin, Dongil (Department of Chemical Engineering, Myongji University)
Publication Information
Korean Chemical Engineering Research / v.57, no.6, 2019 , pp. 781-789 More about this Journal
Abstract
There are frequent accidents by chemicals during laboratory experiments and pilot plant and reactor operations. It is necessary to find and comprehend relevant information to prevent accidents before starting synthesis experiments. In the process design stage, reaction information is also necessary to prevent runaway reactions. Although there are various sources available for synthesis information, including the Internet, it takes long time to search and is difficult to choose the right path because the substances used in each synthesis method are different. In order to solve these problems, we propose an intelligent synthetic path search system to help researchers shorten the search time for synthetic paths and identify hazardous intermediates that may exist on paths. The system proposed in this study automatically updates the database by collecting information existing on the Internet through Web scraping and crawling using Selenium, a Python package. Based on the depth-first search, the path search performs searches based on the target substance, distinguishes hazardous chemical grades and yields, etc., and suggests all synthetic paths within a defined limit of path steps. For the benefit of each research institution, researchers can register their private data and expand the database according to the format type. The system is being released as open source for free use. The system is expected to find a safer way and help prevent accidents by supporting researchers referring to the suggested paths.
Keywords
Chemical Accident; Synthetic Path; Graph Algorithm; Web Scraping; Intelligent Search System;
Citations & Related Records
연도 인용수 순위
  • Reference
1 National Fire Agency, 2018 Dangerous Goods Statistics Data, 121-139(2018).
2 Korea Occupational Safety and Health Agency, Accident Investigation on the Explosion During Butadiene Experiment, 2016-Specialty-424(2016).
3 Szymkuc, S., Gajewska, E. P., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M. and Grzybowski, B. A., "Computer-Assisted Synthetic Planning: The End of the Beginning," Angew. Chem. Int. Ed., 55(20), 5904-5937(2016).   DOI
4 Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., Wang, J., Yu, B., Zhang J. and Bryant, S. H., "PubChem Substance and Compound Data-Bases," Nucleic Acids Research, 44(D1), D1202-D1213(2015).
5 Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B. and Overington, J. P., "ChEMBL: a Large-scale Bioactivity Database for Drug Discovery," Nucleic Acids Research, 40(1), D1100-D1107(2011).
6 Kouranov, A., Xie, L., de la Cruz, J., Chen, L., Westbrook, J., Bourne, P. E. and Berman, H. M., "The RCSB PDB Information Portal for Structural Genomics," Nucleic Acids Research, 34(1), D302-D305(2006).   DOI
7 Pence, H. E. and Williams, A., "ChemSpider: An Online Chemical Information Resource," Journal of Chemical Education, 87(11), 1123-1124(2010).   DOI
8 MOLBASE homepage, http://www.molbase.com/.
9 ChemSrc homepage, https://www.chemsrc.com/en/.
10 Landrum, G., RDKit Documentation, Release (2017).
11 Mitchell, R., Web Scraping with Python: Collecting More Data from the Modern Web, 2nd ed., O'Reilly Media, Inc., Sebastopol (2018).
12 O'Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T. and Hutchison, G. R., "Open Babel: An Open Chemical Toolbox," Journal of Cheminformatics, 3(1), 33(2011).   DOI
13 Homer, R. W., Swanson, J., Jilek, R. J., Hurst, T. and Clark, R. D., "SYBYL Line Notation (SLN): a Single Notation to Represent Chemical Structures, Queries, Reactions, and Virtual Libraries," Journal of Chemical Information and Modeling, 48(12), 2294-2307(2008).   DOI
14 DAYLIGHT Chemical Information Systems, SMARTS - A Language for Describing Molecular Patterns, http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html.
15 DAYLIGHT Chemical Information Systems, A Reaction Transform Language, http://www.daylight.com/dayhtml/doc/theory/theory.smirks.html.
16 Neapolitan, R. E., Foundations of Algorithms, 5th ed., Jones & Bartlett Learning, Burlington(2015).
17 Panico, R., Powell, W. H. and Richer, J. C., "A Guide to IUPAC Nomenclature of Organic Compounds," Blackwell Scientific Publications, Oxford, (1993).
18 Heller, S. R., McNaught, A., Pletnev, I., Stein, S. and Tchekhovskoi, D., "InChI, The IUPAC International Chemical Identifier," Journal of Cheminformatics, 7(1), 23(2015).   DOI
19 Weininger, D., "SMILES, a Chemical Language and Information System I. Introduction to Methodology and Encoding Rules," Journal of Chemical Information and Computer Sciences, 28(1), 31-36(1988).   DOI
20 Dittmar, P. G., Stobaugh, R. E. and Watson, C. E., "The Chemical Abstracts Service Chemical Registry System I. General design," Journal of Chemical Information and Computer Sciences, 16(2), 111-121(1976).
21 OSHA, List of Highly Hazardous Chemicals, Toxics and Reactives (Mandatory), https://www.osha.gov/law-regs.html.
22 NCI/CADD Group, Chemical Identifier Resolver, https://cactus.nci.nih.gov/chemical/structure.
23 CAS, SciFinder, https://www.cas.org/products/scifinder.
24 ChemAxon, MarvinSketch, https://chemaxon.com/products/marvin.