This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
High-Throughput Ligand Screening via Preclustering and Evolved Neural Networks
July-September 2007 (vol. 4 no. 3)
pp. 476-484
The pathway for novel lead drug discovery has many major deficiencies, the most significant of which is the immense size of small molecule diversity space. Methods that increase the search efficiency and/or reduce the size of the search space, increase the rate at which useful lead compounds are identified. Artificial neural networks optimized via evolutionary computation provide a cost and time-effective solution to this problem. Here, we present results that suggest preclustering of small molecules prior to neural network optimization is useful for generating models of quantitative structure-activity relationships for a set of HIV inhibitors. Using these methods, it is possible to prescreen compounds to separate active from inactive compounds or even actives and mildly active compounds from inactive compounds with high predictive accuracy while simultaneously reducing the feature space. It is also possible to identify "human interpretable" features from the best models that can be used for proposal and synthesis of new compounds in order to optimize potency and specificity.

[1] PhRMA Industry Profile 2003 Report, http://www.phrma.orgpublications, 2003.
[2] Center for Drug Evaluation and Research, http://www.fda. gov/cderrdmt, 2006.
[3] C. Hansch and T. Fujita, “$\rho$ -$\sigma$ -$\pi$ Analysis: A Method for the Correlation of Biological Activity and Chemical Structure,” J. Am. Chemical Soc., vol. 86, p. 1616, 1964.
[4] G.E. Kellogg and S.F. Semus, “3D QSAR in Modern Drug Design in Hillisch,” Modern Methods of Drug Discovery, pp. 223-241, 2003.
[5] E. Ernesto, G. Patlewicz, and U. Eugenio, “From Molecular Graphs to Drugs: A Review on the Use of Topological Indices in Drug Design and Discovery,” Indian J. Chemistry, Section A; Inorganic, Bioinorganic, Physical, Theoretical and Analytical Chemistry, vol. 42A, pp. 1315-1329, 2003.
[6] P.V. Desai and E.C. Coutinho, “QSAR in Drug Discovery and Development,” Asian Chemistry Letters, vol. 5, pp. 77-86, 2001.
[7] V.J. Gillet, “Application of Evolutionary Algorithms to Combinatorial Library Design,” Computing Approaches in Chemistry, pp. 1-30, 2003.
[8] S.P. Niculescu, “Artificial Neural Networks and Genetic Algorithms in QSAR,” J. Molecular Structure, vol. 622, pp. 71-83, 2003.
[9] M.J. Embrechts, M. Ozdemir, L. Lockwood, C. Breneman, K. Bennett, D. Devogelaere, and M. Rijckaert, “Feature Selection Methods Based on Genetic Algorithms for in Silico Drug Design,” Evolutionary Computation in Bioinformatics, pp. 317-339, 2002.
[10] D. Weekes and G.B. Fogel, “Evolutionary Optimization, Backpropagation, and Data Preparation Issues in QSAR Modeling of HIV Inhibition by HEPT Derivatives,” BioSystems, vol. 72, pp. 149-158, 2003.
[11] D.G. Landavazo, G.B. Fogel, and D.B. Fogel, “Quantitative Structure-Activity Relationships by Evolved Neural Networks for the Inhibition of Dihydrofolate Reductase by Pyrimidines,” BioSystems, vol. 65, pp. 37-47, 2002.
[12] S.-S. So and M. Karplus, “Evolutionary Optimization in Quantitative Structure-Activity Relationships: An Application of Genetic Neural Networks,” J. Medical Chemistry, vol. 39, pp. 1521-1530, 1996.
[13] B.T. Luke, “Evolutionary Programming Applied to the Development of Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships,” J. Chemical Information and Computing Science, vol. 34, pp. 1279-1287, 1994.
[14] http:/dtp.nci.nih.gov/, 2006.
[15] H. Hong, N. Neamati, S. Wang, M.C. Nicklaus, A. Mazumder, H. Zhao, T.R. Burke, Y. Pommier, and G.W.A. Milne, “Discovery of HIV-1 Integrase Inhibitors by Pharmacophore Searching,” J.Medical Chemistry, vol. 40, pp. 930-936, 1997.
[16] N. Neamati, H. Hong, A. Mazumder, S. Wang, S. Sunder, M.C. Nicklaus, G.W.A. Milne, B. Proksa, and Y. Pommier, “Depsides and Depsidones as Inhibitors of HIV-1 Integrase: Discovery of Novel Inhibitors through 3D Database Searching,” J. Medical Chemistry, vol. 40, pp. 942-951, 1997.
[17] S.Y. Tamura, P.A. Bacha, H.S. Gruver, and R.F. Nutt, “Data Analysis of High-Throughput Screening Results: Application of Multidomain Clustering to the NCI Anti-HCV Data Set,” J.Medical Chemistry, vol. 45, pp. 3082-3093, 2002.
[18] M.C. Nicklaus, N. Neamati, H. Hong, A. Mazumder, S. Sunder, J. Chen, G.W.A. Milne, and Y. Pommier, “HIV-1 Integrase Pharmacophore: Discovery of Inhibitors through Three-Dimensional Database Searching,” J. Medical Chemistry, vol. 40, pp. 920-929, 1997.
[19] C.Y.C. Ma, S.W.M. Wong, D. Hecht, and G. Fogel, “Evolved Neural Networks for High Throughput Anti-HIV Ligand Screening,” Proc. IEEE 2006 Congress on Evolutionary Computation, 2006.
[20] http://dtp.nci.nih.gov/docs/aidsaids_data.html , 2006.
[21] www.cambridgesoft.com, 2006.
[22] P. Willett, “Chemical Similarity Searching,” J. Chemical Information and Computing Science, vol. 38, pp. 983-996, 1998.
[23] E.M. Duffy and W.L. Jorgensen, “Prediction of Properties from Simulations: Free Energies of Solvation in Hexadecane, Octanol, and Water,” J. Am. Chemistry Soc., vol. 122, pp. 2878-2888, 2000.
[24] W.L. Jorgensen and E.M. Duffy, “Prediction of Drug Solubility from Monte Carlo Simulations,” Bioorganic and Medical Chemistry Letters, vol. 10, pp. 1155-1158, 2000.
[25] http:/www.chemcomp.com/, 2006.
[26] X. Yao, “Evolving Neural Networks,” Proc. IEEE, vol. 87, pp. 1423-1447, 1999.
[27] D.B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, third ed. IEEE Press, 2006.
[28] R.S. Pearlman and K.M. Smith, “Novel Software Tools for Chemical Diversity,” Perspectives in Drug Discovery and Design, pp. 339-353, 1998.
[29] S.A. Wildman and G.M. Crippen, “Prediction of Physiochemical Parameters by Atomic Contributions,” J. Chemistry Information and Computer Science, vol. 39, pp. 868-873, 1999.
[30] M. Petitjean, “Applications of the Radius-Diameter Diagram to the Classification of Topological and Geometrical Shapes of Chemical Compounds,” J. Chemistry Information and Computer Science, vol. 32, pp. 331-337, 1992.
[31] L.H. Hall and L.B. Kier, “The Molecular Connectivity Chi Indices and Kappa Shape Indices in Structure-Property Modeling,” Rev. Computing Chemistry, vol. 2, pp. 367-422, 1991.
[32] L.H. Hall and L.B. Kier, “The Nature of Structure-Activity Relationships and Their Relation to Molecular Connectivity,” European J. Medical Chemistry, vol. 12, p. 307, 1977.
[33] T.I. Oprea, “Property Distribution of Drug-Related Chemical Databases,” J. Computer-Aided Molecular Design, vol. 14, pp. 251-264, 2000.
[34] J. Gasteiger and M. Marsili, “Iterative Partial Equalization of Orbital Electronegativity—A Rapid Access to Atomic Charges,” Tetrahedron, vol. 36, p. 3219, 1980.
[35] P. Ertl, B. Rohde, and P. Selzer, “Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties,” J.Medical Chemistry, vol. 43, pp. 3714-3717, 2000.
[36] M. Yazdanian, S.L. Glynn, J.L. Wright, and A. Hawi, “Correlating Partitioning and Caco-2 Cell Permeability of Structurally Diverse Small Molecular Weight Compounds,” Pharmaceutical Research, vol. 15, pp. 1490-1494, 1998.
[37] J.D. Irvine, L. Takahashi, K. Lockhart, J. Cheong, J.W. Tolan, H.E. Selick, and J.R. Grove, “MDCK (Madin-Darby Canine Kidney) Cells: A Tool for Membrane Permeability Screening,” J. Pharmaceutical Science, vol. 88, pp. 28-33, 1999.
[38] P. Stenberg, U. Norinder, K. Luthman, and P. Artursson, “Experimental and Computational Screening Models for the Prediction of Intestinal Drug Absorption,” J. Medical Chemistry, vol. 44, pp. 1927-1937, 2001.
[39] R.O. Potts and R.H. Guy, “Predicting Skin Permeability,” Pharmaceutical Research, vol. 9, pp. 663-669, 1992.
[40] R.O. Potts and R.H. Guy, “Predicting Skin Permeability: II. The Effects of Molecular Size and Hydrogen Bond Activity,” Pharmaceutical Research, vol. 12, pp. 1628-1633, 1995.
[41] G. Colmenarejo, A. Alvarez-Pedraglio, and J.-L. Lavandera, “Cheminformatic Models to Predict Binding Affinities to Human Serum Albumin,” J. Medical Chemistry, vol. 44, pp. 4370-4378, 2001.
[42] E.M. Duffy and W.L. Jorgensen, “Prediction of Properties from Simulations: Free Energies of Solvation in Hexadecane, Octanol, and Water,” J. Am. Chemistry Soc., vol. 122, pp. 2878-2888, 2000.
[43] W.L. Jorgensen and E.M. Duffy, “Prediction of Drug Solubility from Monte Carlo Simulations,” Bioorganic Medical Chemistry Letters, vol. 10, pp. 1155-1158, 2002.
[44] W.L. Jorgensen and E.M. Duffy, “Prediction of Drug Solubility from Structure,” Advanced Drug Delivery Rev., vol. 54, pp. 355-366, 2002.

Index Terms:
Computational intelligence, evolutionary computation, artificial neural networks, medicine and science
Citation:
David Hecht, Gary Fogel, "High-Throughput Ligand Screening via Preclustering and Evolved Neural Networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 3, pp. 476-484, July-Sept. 2007, doi:10.1109/tcbb.2007.1038
Usage of this product signifies your acceptance of the Terms of Use.