AN ACTIVITY PREDICTION MODEL USING SHAPE-BASED DESCRIPTOR METHOD
DOI:
https://doi.org/10.11113/jt.v78.9245Keywords:
Bioactive Molecules, Multilevel Neighborhoods of Atoms, Shape-based Descriptors, Activity prediction modelAbstract
Similarity searching, the activity of an unknown compound (target) is predicted through the comparison of an unknown compound with a set of known activities of compounds. The known activities of the most similar compounds are assigned to the unknown compound. Different machine learning methods and Multilevel Neighborhoods of Atoms (MNA) structure descriptors have been applied for the activities prediction. In this paper, we introduced a new activity prediction model with Shape-Based Descriptor Method (SBDM) .Experimental results show that SBDM-MNA provides a useful method of using the prior knowledge of target class information (active and inactive compounds) of predicting the activity of orphan compounds. To validate our method, we have applied the SBDM-MNA to different established data sets from literature and compare its performance with the classical MNA descriptor for activity prediction.Â
References
M. A. Johnson and G. M. Maggiora. 1990. Concepts And Applications Of Molecular Similarity. Wiley Interdiscip. Rev. Mol. Sci.
C. L. Brace, J. L. Melville, S. D. Pickett, and J. D. Hirst. 2007. Contemporary QSAR Classifiers Compared. J. Chem. Inf. Model. 47(1): 219-227.
F. R. Burden and D. a Winkler. 1999. New {QSAR} Methods Applied to {Structure−Activity} Mapping and Combinatorial Chemistry. J. Chem. Inf. Comput. Sci. 39(2): 236-242.
D. Rogers and a J. Hopfinger. 1994. Application of Genetic Function Approximation To Quantitative Structure-Activity Relationships And Quantitative Structure-Property Relationships. J. Chem. Inf. Comput. Sci. 34: 854-866.
W. P. Walters and B. B. Goldman. 2005. Feature Selection In Quantitative Structure-Activity Relationships. Curr. Opin. Drug Discov. Devel. 8(3): 329-333.
J. J. Sutherland, L. a. O’Brien, and D. F. Weaver. 2004. A Comparison Of Methods For Modeling Quantitative Structure-Activity Relationships. J. Med. Chem. 47(22): 5541-5554.
R. P. Sheridan and S. K. Kearsley. 2002. Why Do We Need So Many Chemical Similarity Search Methods? Drug Discov. Today. 7(17): 903-911.
H. Ding, I. Takigawa, H. Mamitsuka, and S. Zhu. 2013. Similarity-based Machine Learning Methods For Predicting Drug-Target Interactions: A Brief Review. Brief. Bioinform. 15(5): bbt056–.
C. Helma, T. Cramer, S. Kramer, and L. De Raedt. 2004. Data Mining And Machine Learning Techniques For The Identification Of Mutagenicity Inducing Substructures And Structure Activity Relationships Of Noncongeneric Compounds. J. Chem. Inf. Comput. Sci. 44(4): 1402-1411.
J. L. Jenkins, A. Bender, and J. W. Davies. 2006. In silico Target Fishing: Predicting Biological Targets From Chemical Structure. Drug Discov. Today Technol. 3(4): 413-421.
J. J. Sutherland and D. F. Weaver. 2004. Three-dimensional Quantitative Structure-Activity And Structure-Selectivity Relationships Of Dihydrofolate Reductase Inhibitors. J. Comput. Aided. Mol. Des. 18(5): 309-331.
P. Chavatte, S. Yous, C. Marot, N. Baurin, and D. Lesieur, 2001. Three-dimensional Quantitative Structure-Activity Relationships Of Cyclo-Oxygenase-2 (COX-2) Inhibitors: A Comparative Molecular Field Analysis. J. Med. Chem. 44(20): 3223-30.
G. Harper, J. Bradshaw, J. C. Gittins, D. V. S. Green, and A. R. Leach. 2001. Prediction of Biological Activity for High-Throughput Screening Using Binary Kernel Discrimination. J. Chem. Inf. Model. 1295-1300.
P. Willett, D. Wilton, B. Hartzoulakis, R. Tang, J. Ford, and D. Madge. 2007. Prediction Of Ion Channel Activity Using Binary Kernel Discrimination. J. Chem. Inf. Model. 47(5): 1961-1966.
X. Xia, E. G. Maliski, P. Gallant, and D. Rogers. 2004. Classification Of Kinase Inhibitors Using A Bayesian Model. J.Med.Chem. 47: 4463-4470.
G. Schneider and P. Wrede. 1998. Artificial Neural Networks For Computer-Based Molecular Design. Prog. Biophys. Mol. Biol. 70(3): 175-222.
J. J. Sutherland, L. a O. Brien, and D. F. Weaver. 2003. Spline-Fitting with a Genetic Algorithm : A Method for Developing Classification Structure - Activity Relationships. J. Chem. Inf. Model. 1906-1915.
D. a Winkler and F. R. Burden. 2002. Application Of Neural Networks To Large Dataset QSAR, Virtual Screening, And Library Design. Methods Mol. Biol. 201: 325-367.
Z. R. Yang. 2004. Biological Applications Of Support Vector Machines. Brief.Bioinform. 5(4): 328-338.
K. Kawai, S. Fujishima, and Y. Takahashi. 2008. Predictive Activity Profiling of Drugs by Topological-Fragment-Spectra-Based Support Vector Machines. J. Chem. Inf. Model. 48(6): 1152-1160.
A. M. Wassermann, H. Geppert, and J. Bajorath. 2009. Searching For Target-Selective Compounds Using Different Combinations Of Multiclass Support Vector Machine Ranking Methods, Kernel Functions, And Fingerprint Descriptors. J. Chem. Inf. Model. 49(3): 582-592.
A. Abdo, V. Leclère, P. Jacques, N. Salim, and M. Pupin. 2014. Prediction Of New Bioactive Molecules Using A Bayesian Belief Network. J. Chem. Inf. Model. 54(1): 30-36.
D. Filimonov, V. Poroikov, Y. Borodina, and T. Gloriozova. 1999. Chemical Similarity Assessment through Multilevel Neighborhoods of Atoms:  Definition and Comparison with the Other Descriptors. J. Chem. Inf. Comput. Sci. 39(4): 666-670.
I. I. Baskin, N. I. Zhokhova, V. a Palyulin, a N. Zefirov, and N. S. Zefirov. 2009. Multilevel Approach To The Prediction Of Properties Of Organic Compounds In The Framework Of The QSAR/QSPR Methodology. Dokl. Chem. 427(1): 172-175.
H. Hentabli, F. Saeed, A. Abdo, and N. Salim. 2014. A New Graph-Based Molecular Descriptor Using The Canonical Representation Of The Molecule. Sci. World J.
H. Hentabli, N. Salim, A. Abdo, and F. Saeed. 2012. LWDOSM : Language for Writing Descriptors. Adv. Mach. Learn. Technol. Appl. Springer Berlin Heidelb. 247-256.
H. Hentabli, N. Salim, A. Abdo, and F. Saeed. 2013. LINGO-DOSM : LINGO for Descriptors of Outline. Intell. Inf. Database Syst. Springer Berlin Heidelb. 315-324.
S. Larabi, S. Bouagar, F. M. Trespaderne, and E. D. Lopez. 2003. LWDOS: Language For Writing Descriptors Of Outline Shapes. Image Anal. Proc. 2749: 1014-1021.
A. Abdo, B. Chen, C. Mueller, N. Salim, and P. Willett. 2005. Ligand-based Virtual Screening Using Bayesian Networks. J. Chem. Inf. Model. 50(6): 1012-1020.
M. S. Keys. MDL Information Systems Inc. San Leandro, CA.
A. Abdo, S. Caboche, V. Leclère, P. Jacques, and M. Pupin. 2012. A New Fingerprint To Predict Nonribosomal Peptides Activity. J. Comput. Aided. Mol. Des. 26(10): 1187-94.
A. a., L. V., J. P., S. N., and P. M. 2014. Prediction Of New Bioactive Molecules Using A Bayesian Belief Network. J. Chem. Inf. Model. 54(1): 30-36.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
