Group Contribution Method-based Multi-objective Evolutionary Molecular Design

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The search for compounds exhibiting desired physical and chemical properties is an essential, yet complex problem in the chemical, petrochemical, and pharmaceutical industries. During the formulation of this optimization-based design problem two tasks must be taken into consideration: the automated generation of feasible molecular structures and the estimation of macroscopic properties based on the resultant structures. For this structural characteristic-based property prediction task numerous methods are available. However, the inverse problem, the design of a chemical compound exhibiting a set of desired properties from a given set of fragments is not so well studied. Since in general design problems molecular structures exhibiting several and sometimes conflicting properties should be optimized, we proposed a methodology based on the modification of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The originally huge chemical search space is conveniently described by the Joback estimation method. The efficiency of the algorithm was enhanced by soft and hard structural constraints, which expedite the search for feasible molecules. These constraints are related to the number of available groups (fragments), the octet rule and the validity of the branches in the molecule. These constraints are also used to introduce a special genetic operator that improves the individuals of the populations to ensure the estimation of the properties is based on only reliable structures. The applicability of the proposed method is tested on several benchmark problems.

[1] Camarda, K.V.; Maranas, C.D.: Optimization in polymer design using connectivity indices, Ind. Engng. Chem. Res., 1999 38(5), 1884–1892 DOI: 10.1021/ie980682n

[2] Kasat, R.B.; Ray, A.K.; Gupta, S.K.: Applications of genetic algorithms in polymer science and engineering, Mat. Manufact. Proc., 2003 18(3), 523–532 DOI: 10.1081/AMP-120022026

[3] Perdomo, F.A.; Perdomo, L.; Millán, B.M.; Aragón, J.L.: Design and improvement of biodiesel fuel blends by optimization of their molecular structures and compositions, Chem. Engng. Res. Design, 2014 92(8), 1482–1494 DOI: 10.1016/j.cherd.2014.02.011

[4] Joback, K.G.: Computer aided molecular design (CAMD): Designing better chemical products. (Molecular Knowledge Systems Inc., Bedford, NH U.S.A.) 1998-2016

[5] Schneider, G.; Hartenfeller, M.; Reutlinger, M.; Tanrikulu, Y.; Proschak, E.; Schneider, P.: Voyages to the (un)known: Adaptive design of bioactive compounds, Trends Biotechn., 2009 27(1), 18–26 DOI: 10.1016/j.tibtech.2008.09.005

[6] Gani, R.; Achenie, L.E.K.; Venkatasubramanian, V.: Introduction to CAMD in computer aided chemical engineering (Eds.: Luke, R.G.; Achenie, L.E.K.; Venkat, V.: Elsevier, Amsterdam, The Netherlands), 2002 Chapter 1, pp. 3–21 DOI: 10.1016/S1570-7946(03)80003-2

[7] Gani, R.; Jiménez-González, C.; Constable, D.J.C.: Method for selection of solvents for promotion of organic reactions, Comp. Chem. Engng., 2005 29(7), 1661–1676 DOI: 10.1016/j.compchemeng.2005.02.021

[8] Camarda, K.V.; Bonnell, B. W., Maranas, C. D., Nagarajan, R.: Design of surfactant solutions with optimal macroscopic properties, Comp. Chem. Engng., 1999 23(Supplement), S467–S470 DOI: 10.1016/S0098-1354(99)80115-X

[9] Sahinidis, N.V.; Tawarmalani, M.; Yu, M.: Design of alternative refrigerants via global optimization, AIChE J., 2003 49(7), 1761–1775 DOI: 10.1002/aic.690490714

[10] McLeese, S.E.; Eslick, J.C.; Hoffmann, N.J.; Scurto, A.M.; Camarda, K.V.: Design of ionic liquids via computational molecular design, Comp. Chem. Engng., 2010 34(9), 1476–1480 DOI: 10.1016/j.compchemeng.2010.02.017

[11] Gani, R.: Computer-aided methods and tools for chemical product design, Chem. Engng. Res. Design, 2004 82(11), 1494–1504 DOI: 10.1205/cerd.82.11.1494.52032

[12] Holenda, B.; Holenda, B.; Dallos, A.; Nagy, Á.; Friedler, F.; Fan, L.-T.: A combinatorial approach for generating environmentally benign solvents and separation agents, Chem. Eng. Trans., Ser., 2003 3, 871–875

[13] Klamt, A.: Conductor-like screening model for real solvents: A new approach to the quantitative calculation of solvation phenomena, J. Phys. Chem., 1995 99(7), 2224–2235 DOI: 10.1021/j100007a062

[14] Friedler, F.; Fan, L.T.; Kalotai, L.; Dallos, A.: A combinatorial approach for generating candidate molecules with desired properties based on group contribution, Comp. Chem. Engng., 1998 22(6), 809–817 DOI:10.1016/S0098-1354(97)00253-6

[15] Lin, B.; Chavali, S.; Camarda, K.; Miller, D.C.: Computer-aided molecular design using Tabu search, Comp. Chem. Engng., 2005 29(2), 337–347 DOI: 10.1016/j.compchemeng.2004.10.008

[16] Soto, A.J.; Cecchini, R.L.; Vazquez, G.E.; Ponzoni, I.: Multi-objective feature selection in QSAR using a machine learning approach, QSAR & Comb. Sci., 2009 28(11–12), 1509–1523 DOI: 10.1002/qsar.200960053

[17] Hii, C.E.A.: Evolving toxicity models using multigene symbolic regression and multiple objectives, Int. J. Mach. Learn. Comp., 2011 1, 30–35 DOI: 10.7763/IJMLC.2011.V1.5

[18] Manoharan, P.E.A.: Rationalizing fragment-based drug discovery for BACE1: Insights from FB-QSAR, FB-QSSR, multi-objective-QSPR, and MIF studies, J. Comput. Aided Mol. Des., 2010 24, 843–864 DOI: 10.1007/s10822-010-9378-9

[19] Herring III, R.H.; Eden, M.R.: Evolutionary algorithm for de novo molecular design with multi-dimensional constraints, Comp. Chem. Engng., 2015 83, 267–277 DOI: 10.1016/j.compchemeng.2015.06.012

[20] Weber, L.: Evolutionary combinatorial chemistry: application of genetic algorithms, Drug Discovery Today, 1998 3(8), 379–385 DOI: 10.1016/S1359-6446(98)01219-7

[21] Venkatasubramanian, V.; Sundaram, A.; Chan, K.; Caruthers, J.M.: Computer-aided molecular design using neural networks and genetic algorithms. Genetic algorithms in molecular modeling (Ed.: Devillers, J.: Academic Press, London, UK) 1996 DOI: 10.1016/B978-012213810-2/50012-8

[22] Nicolaou, C.A.; Brown, N.: Multi-objective optimization methods in drug design, Drug Discovery Today: Technologies, 2013 10(3), e427–e435 DOI: 10.1016/j.ddtec.2013.02.001

[23] Joback, K.G.; Reid, R.C.: Estimation of pure-component properties from group-contributions, Chem. Engng. Commun., 1987 57(1–6), 233–243 DOI: 10.1080/00986448708960487

[24] Shin Hyo Bang, S.J.L.; Taeseon Y.: Boiling point estimation program especially for aromatic compounds supplementing the Joback method, Int. J. Chem. Engng. Appl., 2014 5(4), 331–334 DOI: 10.7763/IJCEA.2014.V5.404

[25] Deb, K.; Agrawal, S.; Pratap, A.; Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, in Parallel problem solving from nature PPSN (Eds.: Schoenauer VI, M.: Deb, K.; Rudolph, G.; Yao, X.; Lutton, E.; Merelo, J. J.; Schwefel, H.-P., Springer, Berlin, Germany) 2000 pp. 849–858 DOI: 10.1007/3-540-45356-3_83

[26] Deb, K.: Multi-objective genetic algorithms: problem difficulties and construction of test problems, Evolut. Comp., 1999 7, 205–230 DOI: 10.17877/DE290R-5636

[27] Song, L.: NGPM - A NSGA-II Program in MATLAB, User Manual, 2011

[28] Odele, O.; Macchietto, S.: Computer aided molecular design: A novel method for optimal solvent selection, Fluid Phase Equil., 1993 82, 47–54 DOI: 10.1016/0378-3812(93)87127-M

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