Molecular Structure Elucidation Using Ant Colony Optimization: A Preliminary Study
Pages 120 - 131
Abstract
Identifying the structure of unknown molecules is an important activity in the pharmaceutical industry where it underpins the production of new drugs and the analysis of complex biological samples. We present here a new method for automatically identifying the structure of an unknown molecule from its nuclear magnetic resonance (NMR) spectrum. In the technique, an ant colony optimization algorithm is used to search iteratively the highly-constrained space of feasible molecular structures, evaluating each one by reference to NMR information on known molecules stored (in a raw form) in a database. Unlike existing structure elucidation systems, ours: does not need prior training or use spectrum prediction; does not rely on expert rules; and avoids enumeration of all possible candidate structures. We describe the important elements of the system here and include results on a <em>preliminary</em>test set of molecules. Whilst the results are currently too limited to allow parameter studies or comparison to other methods, they nevertheless indicate the system is working acceptably and shows considerable promise.
References
[1]
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004).
[2]
Aha, D.: Lazy Learning. Kluwer Academic Publishers, Norwell (1997).
[3]
Stützle, T., Hoos, H.: MAX-MIN Ant system and local search for combinatorial optimization problems. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, 313-329 (1999).
[4]
Gambardella, L., Dorigo, M.: An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem. INFORMS Journal on Computing 12(3), 237-255 (2000).
[5]
http://www.chem.qmul.ac.uk/iupac/
[6]
Stützle, T., Hoos, H.: MAX-MIN Ant System. Future Generation Computer Systems 16(8), 889-914 (2000).
[7]
Farrelly, C.: From Spectrum to Structure Using Machine Learning. PhD thesis, School of Chemistry, University of Manchester, UK (2008).
[8]
Munkres, J.: Algorithms for the Assignment and Transportation Problems. Journal of the Society of Industrial and Applied Mathematics 5(1), 32-38 (1957).
[9]
MOLGEN Tool, http://molgen.de/?src=documents/molgenonline
[10]
Griffiths, L., Bright, J.: Towards the automatic analysis of 1H NMR spectra: Part 3. Confirmation of postulated chemical structure. Magn. Reson. Chem. 40, 623-634 (2002).
[11]
Carhart, R., Smith, D., Brown, H., Djerassi, C.: Applications of artificial intelligence for chemical inference. XVII. Approach to computer-assisted elucidation of molecular structure. Journal of the American Chemical Society 97(20), 5755-5762 (1975).
[12]
Carhart, R., Smith, D., Gray, N., Nourse, J., Djerassi, C.: GENOA: A Computer Program for Structure Elucidation Utilizing Overlapping and Alternative Substructures. J. Org. Chem. 46, 1708-1718 (1981).
[13]
Sasaki, S., Kudo, Y.: Structure elucidation system using structural information from multisources: CHEMICS. Journal of Chemical Information and Computer Sciences 25(3), 252-257 (1985).
[14]
Elyashberg, M., Martirosian, E., Karasev, Y., Thiele, H., Somberg, H.: X-PERT: a user-friendly expert system for molecular structure elucidation by spectral methods. Analytica Chimica Acta 337(3), 265-286 (1997).
[15]
Elyashberg, M., Blinov, K., Williams, A., Martirosian, E., Martin, G.: Application of a New Expert System for the Structure Elucidation of Natural Products from the 1D and 2D NMR Data. J. Nat. Prod. 65(5), 693-703 (2002).
[16]
Meiler, J., Will, M.: Genius: A genetic algorithm for automated structure elucidation from C-13 NMR spectra. Journal of the American Chemical Society 124(9), 1868-1870 (2002).
[17]
Kell, D.: Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discovery Today 11(23-24), 1085-1092 (2006).
[18]
Kell, D.: Metabolomic biomarkers: search, discovery and validation. Exp Rev Mol Diagn 7(4), 329-333 (2007).
- Molecular Structure Elucidation Using Ant Colony Optimization: A Preliminary Study
Recommendations
In silico 3-D structure prediction and molecular docking studies of inosine monophosphate dehydrogenase from Plasmodium falciparum
Display Omitted Growing resistance in malarial parasites, particularly in Plasmodium falciparum needs for development of novel drug targets.Inosine monophosphate dehydrogenase (IMPDH) is an important target for antimalarial drug discovery.A homology ...
Structure-based drug design of novel M. tuberculosis InhA inhibitors based on fragment molecular orbital calculations
Abstract2-trans enoyl-acyl carrier protein reductase (InhA) is a promising target for developing novel chemotherapy agents for tuberculosis, and their inhibitory effects on InhA activity were widely investigated by the physicochemical ...
Graphical abstractOutline of proposing novel potent InhA inhibitors based on our ab initio molecular simulations based on protein-ligand docking and fragment molecular orbital methods.
Display Omitted
Highlights- Interactions between 2-trans enoyl-acyl carrier protein and its inhibitors were studied.
Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model
IPDPS '05: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07The Protein Folding Problem studies the way in which a protein - a chain of amino acids - will 'fold' into its natural state. Predicting the way in which various proteins fold can be fundamental in developing treatments of diseases such as Alzeihmers ...
Comments
Information & Contributors
Information
Published In
September 2008
414 pages
ISBN:9783540875260
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 22 September 2008
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 19 Feb 2025