(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-10 Issue-46 January-2020
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Paper Title : An approach for extracting chemical data from molecular representations
Author Name : Amena Mahmoud, Taher Hamza and M. Z. Rashad
Abstract :

The ever-increasing quantity of chemical literature necessitates the creation of automated techniques for extracting relevant information. The digital conversion process of chemical molecule representations into their corresponding computerized illustration has been evolved using numerous applications. This work is a part of our contribution to computer-aided toxicity recognition research which predicts the possible toxic side effects of drugs on the process of drug design. The current proposed approach provides essential reviews for previous related researches in the field of automated chemical information extraction and mentions our current proposed technique that is considered as an intelligent module for converting chemical molecule structures to computerized structures. Mentioned mechanisms are used to detect bonds that are represented by letters through comparison with templates database, atoms, and lines for extracting data from graphs. A sample of 100 chemical compound structures was used to be converted into computer representation to get an overall result of 88.9% precision. Finally, a comparison between related approaches and the current proposed one at their precision rates for classification of substructure patterns was conducted.

Keywords : Chemical molecules, Data extraction, Bonds, Atoms, Molecule representation.
Cite this article : Mahmoud A, Hamza T, Rashad M. An approach for extracting chemical data from molecular representations. International Journal of Advanced Computer Research. 2020; 10(46):27-33. DOI:10.19101/IJACR.2019.940105.
References :
[1]Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, et al. Pubchem substance and compound databases. Nucleic Acids Research. 2015; 44(D1): D1202-13.
[Crossref] [Google Scholar]
[2]Chen H, Sharp BM. Content-rich biological network constructed by mining pubmed abstracts. BMC Bioinformatics. 2004.
[Crossref] [Google Scholar]
[3]Li Z, Wan H, Shi Y, Ouyang P. Personal experience with four kinds of chemical structure drawing software: review on Chemdraw, Chemwindow, ISIS/draw, and Chemsketch. Journal of Chemical Information and Computer Sciences. 2004; 44(5):1886-90.
[Crossref] [Google Scholar]
[4]Gkoutos GV, Rzepa H, Clark RM, Adjei O, Johal H. Chemical machine vision: automated extraction of chemical metadata from raster images. Journal of Chemical Information and Computer Sciences. 2003; 43(5):1342-55.
[Google Scholar]
[5]Weininger D, Weininger A, Weininger JL. SMILES. 2. algorithm for generation of unique SMILES notation. Journal of Chemical Information and Computer Sciences. 1989; 29(2):97-101.
[Crossref] [Google Scholar]
[6]Heller SR, McNaught A, Pletnev I, Stein S, Tchekhovskoi D. In ChI, the IUPAC international chemical identifier. Journal of Cheminformatics. 2015; 7(23):1-34.
[Crossref] [Google Scholar]
[7]Ofner J, Brenner F, Wieland K, Eitenberger E, Kirschner J, Eisenmenger-Sittner C, et al. Image-based chemical structure determination. Scientific Reports. 2017.
[Google Scholar]
[8]Da Cunha MM, Trepout S, Messaoudi C, Wu TD, Ortega R, Guerquin-Kern JL, et al. Overview of chemical imaging methods to address biological questions. Micron. 2016; 84:23-36.
[Crossref] [Google Scholar]
[9]Ziatdinov M, Dyck O, Maksov A, Li X, Sang X, Xiao K, et al. Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations. ACS Nano. 2017; 11(12):12742-52.
[Crossref] [Google Scholar]
[10]Laanait N, Ziatdinov M, He Q, Borisevich A. Identifying local structural states in atomic imaging by computer vision. Advanced Structural and Chemical Imaging. 2016; 2:1-11.
[Google Scholar]
[11]Mallea MD, Meltzer P, Bentley PJ. Capsule neural networks for graph classification using explicit tensorial graph representations. arXiv preprint arXiv:1902.08399. 2019.
[Google Scholar]
[12]Pitas I. Digital image processing algorithms and applications. John Wiley & Sons; 2000.
[Google Scholar]
[13]Fletcher LA, Kasturi R. A robust algorithm for text string separation from mixed text/graphics images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1988; 10(6):910-8.
[Crossref] [Google Scholar]
[14]Illingworth J, Kittler J. A survey of the hough transform. Computer Vision, Graphics, and Image Processing. 1988; 44(1):87-116.
[Crossref] [Google Scholar]
[15]Filippov IV, Nicklaus MC, Kinney J. Improvements in optical structure recognition application. In IAPR international workshop on document analysis systems, Boston, MA 2010.
[Google Scholar]
[16]Ibison P, Jacquot M, Kam F, Neville AG, Simpson RW, Tonnelier C, et al. Chemical literature data extraction: the CLiDE Project. Journal of Chemical Information and Computer Sciences. 1993; 33(3):338-44.
[Crossref] [Google Scholar]
[17]Algorri ME, Zimmermann M, Friedrich CM, Akle S, Hofmann-Apitius M. Reconstruction of chemical molecules from images. In annual international conference of the IEEE engineering in medicine and biology society 2007 (pp. 4609-12). IEEE.
[Crossref] [Google Scholar]