Abstract
Combinatorial chemistry and high-throughput screening have caused a fundamental shift in the way chemists contemplate experiments. Designing a combinatorial library is a controversial art that involves a heterogeneous mix of chemistry, mathematics, economics, experience, and intuition. Although there seems to be little agreement as to what constitutes an ideal library, one thing is certain: only one property or measure seldom defines the quality of the design. In most real-world applications, a good experiment requires the simultaneous optimization of several, often conflicting, design objectives, some of which may be vague and uncertain. In this paper, we discuss a class of algorithms for subset selection rooted in the principles of multiobjective optimization. Our approach is to employ an objective function that encodes all of the desired selection criteria, and then use a simulated annealing or evolutionary approach to identify the optimal (or a nearly optimal) subset from among the vast number of possibilities. Many design criteria can be accommodated, including diversity, similarity to known actives, predicted activity and/or selectivity determined by quantitative structure-activity relationship (QSAR) models or receptor binding models, enforcement of certain property distributions, reagent cost and availability, and many others. The method is robust, convergent, and extensible, offers the user full control over the relative significance of the various objectives in the final design, and permits the simultaneous selection of compounds from multiple libraries in full- or sparse-array format.
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Thompson, L.A. and Ellman, J.A., Chem. Rev., 96 (1996) 555–600.
Pareto, V., Manual of Political Economy, 1906, p. 106.
Eschenauer, H.A., Koski, J. and Osyczka, A., Multicriteria Design Optimization: Procedures and Applications, Springer-Verlag, New York (1986).
Martin, E.J., Blaney, J.M., Siani, M.A., Spellmeyer, D.C., Wong, A.K. and Moos, W.H., J. Med. Chem. 38, (1995) 1431–1436.
Agrafiotis, D.K., Bone, R.F., Salemme, F.R. and Soll, R.M., U.S. Patents 5,463,564, (1995); 5,574,656, (1996); 5,684,711, (1997); and 5,901,069, (1999).
Sheridan, R.P. and Kearsley, S.K., J. Chem. Inf. Comput. Sci. 35 (1995) 310–320.
Weber, L., Wallbaum, S., Broger, C. and Gubernator, K., Angew. Chem. Int. Ed. Eng., 34 (1995) 2280–2282.
Singh, J., Ator, M.A., Jaeger, E.P., Allen, M.P., Whipple, D.A., Soloweij, J.E., Chowdhary, S. and Treasurywala, A.M., J. Amer. Chem. Soc., 118 (1996) 1669–1676.
Agrafiotis, D.K., Stochastic Algorithms for Maximizing Molecular Diversity, presented at the 3rd Electronic Computational Chemistry Conference, http://hackberry.chem.niu.edu/ECCC3/paper 48, (1996).
Agrafiotis, D.K., J. Chem. Inf. Comput. Sci., 37 (1997) 841–851.
Agrafiotis, D.K., J. Chem. Inf. Comput. Sci., 37 (1997) 576–580.
Agrafiotis, D.K. and Lobanov, V.S., J. Chem. Inf. Comput. Sci., 39 (1999) 51–58.
Hassan, M., Bielawski, J.P., Hempel, J.C. and Waldman, M., Mol. Diversity, 2 (1996) 64–74.
Waldman, M., Li, H. and Hassan, M., Novel Algorithms for the Optimization of Molecular Diversity of Combinatorial Libraries, J. Mol. Graphics Mod., 18 (2000) 412–426.
Good, A.C. and Lewis, R.A., J. Med. Chem., (1997) 40 3926.
Zheng, W., Cho, S.J. and Tropsha, A., J. Chem. Inf. Comput. Sci., 27 (1998) 251.
Brown, R.D. and Martin, Y.C., J. Med. Chem., 40 (1997) 2304–2313.
Gillet, F.J., Willett, P., Bradshaw, J. and Green, D.V.S., J. Chem. Inf. Comput. Sci., 39 (1999) 169–177.
Rassokhin, D.N. and Agrafiotis, D.K., J. Mol. Graphics Mod., 18 (2000) 370–384.
Brown, R.D., Hassan, M. and Waldman, M., J. Mol. Graphics Mod., 18 (2000) 427–437.
Sheridan, R.P., SanFeliciano, S.G. and Kearsley, S.K., J. Mol. Graphics Mod., 18 (2000) 320–334.
Downs, G.M. and Willett, P., J. Chem. Inf. Comput. Sci., 34 (1994) 1094–1102.
Brown, R.D. and Martin, Y.C., J. Chem. Inf. Comput. Sci., 36 (1996) 572–584.
Patterson, D.E., Cramer, R.D., Ferguson, A.M., Clark, R.D. and Weinberger, L.E., J. Med. Chem., 39 (1996) 3049–3059.
Brown, R.D. and Martin, Y.C., J. Chem. Inf. Comput. Sci., 37 (1997) 1–9.
Matter, H., J. Med. Chem., 40 (1997) 1219.
Martin, Y.C., Bures, M.G. and Brown, R.D., Pharm. Pharmacol. Common.,4 (1998) 147.
Gillet, V.J., Willett, P. and Bradshaw, J., J. Chem. Inf. Comput. Sci., 37 (1997) 731–740.
Jamois, E.A., Hassan, M. and Waldman, M., J. Chem. Inf. Comput. Sci., 40 (2000) 63–70.
Polinsky, A., Feinstien, R.D., Shi, S. and Kuki, A., In Chaiken, I.M. and Janda, K.D. (Eds.), Molecular Diversity and Combinatorial Chemistry, American Chemical Society: Washington, DC, (1996) pp. 219–232.
Martin, E.J., Spellmeyer, D.C., Critchlow, R.E. and Blaney, J.M., In: Lipkowitz, K.B. and Boyd, D.B. (Eds.), Does Combinatorial Chemistry Obviate Computer-Aided Drug Design? Reviews in Computational Chemistry, Volume 10, VCH Publishers, New York, (1997) pp. 75–100.
Willett, P., Similarity and Clustering in Chemical Information Systems, Research Studies Press, Letchworth, UK, (1987).
Taylor, R., J. Chem. Inf. Comput. Sci., 35 (1995) 59–67.
Chapman, D., J. Comput.-Aided Mol. Design, 10 (1996) 501–512.
Cummins, D.J., Andrews, C.W., Bentley, J.A. and Cory, M., J. Chem. Inf. Comput. Sci., 36 (1996) 750–763.
Pearlman, R.S. and Smith, R.S., Perspect. Drug Discovery Design, 9 (1998) 339–353.
Pickett, S., Mason, J.S. and McLay, I.M., J. Chem. Inf. Comput. Sci., 36 (1996) 1214–1223.
Davies, E.K. and Briant, C., Network Sci., (1995) http://www.awod.com/netsci/issues/
Shemetulsksis, N.E., Weininger, D., Blankley, C.J., Yang, J.J. and Humblet, C., J. Chem. Inf. Comput. Sci., 36 (1996) 862–871.
Boyd, S.M., Beverly, M., Norskov, L. and Hubbard, R.E., J. Comput.-Aided Mol. Design, 9 (1995) 417–424.
Agrafiotis, D.K. and Lobanov, V.S., J. Chem. Inf. Comput. Sci., 40 (2000) 1030–1038.
Stanton, R.V., Mount, J. and Miller, J.L., J. Chem. Inf. Comput. Sci., 40 (2000) 701–705.
Agrafiotis, D.K., in Schleyer, P.v.R., Allinger, N.L., Clark, T., Gasteiger, J., Kollman, P.A., Schaefer III, H.F. and Schreiner, P.R. (Eds.), The Diversity of Chemical Libraries, The Encyclopedia of Computational Chemistry, John Wiley & Sons, Chichester, UK, (1998) pp. 742–761.
Agrafiotis, D.K., Myslik, J.C. and Salemme, F.R., Mol. Diversity, 4 (1999) 1–22.
Agrafiotis, D.K., Lobanov, V.S., Rassokhin, D.N. and Izrailev, S., In: Böhm, H.-J. and Schneider, G. (Eds.), The Measurement of Molecular Diversity, Virtual Screening of Bioactive Molecules, Wiley-VCH Verlag GmbH, Weinheim, Germany, (2000).
Lajiness, M.S., in: Silipo, C. and Vittoria, A. (Eds.), QSAR: Rational Approaches to the Design of Bioactive Compounds, Elsevier, Amsterdam, Netherlands, (1991) pp. 201–204.
Dhanoa, D.S., Gupta, V., Sapienza, A. and Soll, R.M., Poster 26, American Chemical Society National Meeting, Anaheim, CA, (1999).
Hall, L.H. and Kier, L.B., In: Boyd, D.B. and Lipkowitz, K.B. (Eds.), The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure-Property Relations," Reviews of Computational Chemistry, VCH Publishers, New York (1991), Ch. 9, pp. 367–422.
Bonchev, D. and Trinajstic, N., J. Chem. Phys., 67 (1977) 4517–4533.
Lobanov, V.S. and Agrafiotis, D.K., J. Chem. Inf. Comput. Sci., 40 (2000) 460–470.
Agrafiotis, D.K., Prot. Sci., 6 (1997) 287–293.
Agrafiotis, D.K. and Lobanov, V.S., J. Chem. Inf. Comput. Sci., 40 (2000) 1356–1362.
Rassokhin, D.N., Lobanov, V.S. and Agrafiotis, D.K., J. Comput. Chem., 22 (2000) 373–386.
Agrafiotis, D.K., Rassokhin, D.N. and Lobanov, V.S., J. Comput. Chem., 22 (2000) 488–500.
Ghose, A.K., Viswanadhan, V.N. and Wendoloski, J.J., J. Phys. Chem. A, 102 (1998) 3762–3772.
Agrafiotis, D.K., J. Chem. Inf. Comput. Sci., 41 (2000) 159–167.
Martin, E.J. and Critchlow, R.E., J. Comb. Chem., 1 (1999) 32–45.
Martin, E.J. and Wong, A., J. Chem. Inf. Comput. Sci., 40 (2000) 215–220.
Koehler, R.T., Dixon, S.L. and Villar, O.H., J.Med. Chem., 42 (1999) 4695–4704.
Lipinski, C.A., Lombardo, F., Dominy, B.W. and Feeny, P.J., Adv. Drug Delivery Rev., 23 (1997) 3–25.
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Agrafiotis, D. Multiobjective optimization of combinatorial libraries. J Comput Aided Mol Des 16, 335–356 (2002). https://doi.org/10.1023/A:1020837112154
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DOI: https://doi.org/10.1023/A:1020837112154