Abstract
In conventional drug design, the drug discovery proceeds largely by trial and error synthesizing thousands of molecules. Although this approach is the most effective method to discover drugs, it is very financially expensive and labor intensive.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amat L, Besalu E, Carbo-Dorca R (2001) Identification of active molecular sites using quantum-self-similarity matrices. J Chem Inf Comput Sci 41:978–991
Bajorath J, Klein TE, Lybrand TP, Novotny J (1999) Computer-aided drug discovery: from target proteins to drug candidates. Proc Pac Symp Biocomput 4:413–414
Bazan J, Skowron A, Synak P (1994) Dynamic reducts as a tool for extracting laws from decision tables. In: Ras ZW, Zemankova M (eds) Proceedings of the 8th symposium on methodologies for intelligent systems. Lecture notes in artificial intelligence, vol 869. Springer, New York, pp 346–355
Bjorvand AT, Komorowski J (1997) Practical applications of genetic algorithms for efficient reduct computation. In: Proceedings of the 15th IMACS world congress on scientific computation, modeling and applied mathematics, vol 4, pp 601–606
Bravi G, Gancia E, Mascagni P, Pegna M, Todeschini R, Zaliani A (1997) MS-WHIM: New 3D theoretical descriptors derived from molecular surface properties: a comparative 3D QSAR study in a series of steroids. J Comput Aided Mol Des 11:79–92
Chen H, Zhou J, Xie G (1998) PARM: a genetic algorithm to predict bioactivity. J Chem Inf Comput Sci 38:243–250
Chen KH, Ras ZW, Skowron A (1988) Attributes and rough properties in information systems. Int J Approx Reason 2:365–376
Chouchoulas A, Shen Q (2001) Rough set-aided keyword reduction for text categorisation. Appl Artif Intell 15(9):843–873
Cornelis C, Jensen R, Martin GH, Slezak D (2010) Attribute selection with fuzzy decision reducts. Inf Sci 180:209–224
Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice Hall, Englewood Cliffs
Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17:191–209
Dubois D, Prade H (1992) Putting fuzzy sets and rough sets together. In: Slowiniski R (ed) Intelligent decision support: handbook of applications and advances of rough sets theory. Kluwer, Dordrecht, pp 203–232
Guha R, Jurs PC (2004) Development of linear, ensemble, and nonlinear models for the prediction and interpretation of the biological activity of a set of PDGFR inhibitors. J Chem Inf Comput Sci 44:2179–2189
Guha R, Jurs PC (2004) Development of QSAR models to predict and interpret the biological activity of artemisinin analogues. J Chem Inf Comput Sci 44:1440–1449
Guyon I (2003) Elisseeff: an introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Hu Q, Xie Z, Yu D (2007) Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recogn 40:3577–3594
Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178:3577–3594
Hu Q, Yu D, Xie Z, Liu J (2007) Fuzzy probabilistic approximation spaces and their information measures. IEEE Trans Fuzzy Syst 14(2):191–201
Jain AN, Koile K, Chapman D (1994) Compass: predicting biological activities from molecular surface properties. Performance comparisons on a steroid benchmark. J Med Chem 37:2315–2327
Jensen R, Shen Q (2004) Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets Syst 141:469–485
Jensen R, Shen Q (2004) Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approach. IEEE Trans Knowl Data Eng 16(12):1457–1471
Jensen R, Shen Q (2007) Fuzzy-rough sets assisted attribute selection. IEEE Trans Fuzzy Syst 15:73–89
Jensen R, Shen Q (2009) New approaches to fuzzy-rough feature selection. IEEE Trans Fuzzy Syst 17(4):824–838
Katritzky AR, Lobanov V, karelson M (1994) Comprehensive descriptors for structural and statistical analysis version 1.1. University of Florida, Florida
Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324
Koller D, Sahami M (1996) Toward optimal feature selection. In: Proceedings of the international conference on machine learning, pp 284–292
Komorowski J, Pawlak Z, Polkowski L, Skowron A (1999) Rough sets: a tutorial. In: Pal SK, Skowron A (eds) Rough-fuzzy hybridization: a new trend in decision making. Springer, Singapore, pp 3–98
Kumar M, Thurow K, Stoll N, Stoll R (2007) Robust fuzzy mappings for QSAR studies. Eur J Med Chem 42:675–685
Leach AR (2001) Molecular modelling: principles and applications, vol 2. Prentice Hall, Reading
Leardi R, Gonzalez AL (1998) Genetic algorithms applied to feature selection in PLS regression: How and when to use them. Chemometr Intell Lab Syst 41:195–207
Li ZR, Han LY, Xue Y, Yap CW, Li H, Jiang L, Chen YZ (2007) MODEL—molecular descriptor lab: a web-based server for computing structural and physicochemical features of compounds. Biotechnol Bioeng 97:96–389
Lin TY (2001) Granulation and nearest neighborhoods: rough set approach. In: Pedrycz W (ed) Granular computing: an emerging paradigm. Physica-Verlag, Heidelberg, pp 125–142
Liu SS, Yin CS, Li ZL, Cai SX (2001) QSAR study of steroid benchmark and dipeptides based on MEDV-13. J Chem Inf Comput Sci 41:321–329
Maji P (2009) \(f\)-Information measures for efficient selection of discriminative genes from microarray data. IEEE Trans Biomed Eng 56(4):1063–1069
Maji P, Garai P (2013) On fuzzy-rough attribute selection: criteria of max-dependency, max-relevance, min-redundancy, and max-significance. Appl Soft Comput 13(9):3968–3980
Maji P, Pal SK (2010) Feature selection using \(f\)-information measures in fuzzy approximation spaces. IEEE Trans Knowl Data Eng 22(6):854–867
Maji P, Paul S (2010) Rough sets for selection of molecular descriptors to predict biological activity of molecules. IEEE Trans Syst Man Cybern Part C Appl Rev 40(6):639–648
Modrzejewski M (1993) Feature selection using rough sets theory. In: Proceedings of the 11th international conference on machine learning, pp 213–226
Neagu CDN, Aptula AO, Gini G (2002) Neural and neuro-fuzzy models of toxic action of phenols. In: Proceedings of the 1st international IEEE symposium on intelligent systems, vol 1, pp 283–288
Ozdemir M, Embrechts MJ, Arciniegas F, Breneman CM, Lockwood L, Bennett KP (2001) Feature selection for in-silico drug design using genetic algorithms and neural networks. In: Proceedings of IEEE mountain workshop on soft computing in industrial applications, pp 25–27
Parthalain N, Shen Q, Jensen R (2010) A distance measure approach to exploring the rough set boundary region for attribute reduction. IEEE Trans Knowl Data Eng 22(3):305–317
Pawlak Z (1991) Rough sets: theoretical aspects of resoning about data. Kluwer, Dordrecht
Polanski J, Walczak B (2000) The comparative molecular surface analysis (COMSA): a novel tool for molecular design. Comput Chem 24:615–625
Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, Mountain View
Quinlan JR (1996) Improved use of continuous attributes in C4.5. J Artif Intell Res 4:77–90
Robert D, Amat L, Carbo-Dorca R (1999) Three-dimensional quantitative structure-activity relationships from tuned molecular quantum similarity measures: prediction of the corticosteroid-binding globulin binding affinity for a steroid family. J Chem Inf Comput Sci 39:333–344
Robinson D, Winn P, Lyne P, Richards W (1999) Self-organizing molecular field analysis: a tool for structure-activity studies. J Med Chem 42:573–583
Shen Q, Chouchoulas A (1999) Combining rough sets and data-driven fuzzy learning for generation of classification rules. Pattern Recogn 32(12):2073–2076
Skowron A, Rauszer C (1992) The discernibility matrices and functions in information systems. In: Slowinski R (ed) Intelligent decision support. Kluwer, Dordrecht, pp 331–362
Skowron A, Swiniarski RW, Synak P (2005) Approximation spaces and information granulation. LNCS Trans Rough Sets 3:175–189
Slezak D (1996) Approximate reducts in decision tables. In: Proceedings of the 6th international conference on information processing and management of uncertainty in knowledge-based systems, pp 1159–1164
Sventik V, Wang T, Tong C, Liaw A, Sheridan RP, Song Q (2005) Boosting: an ensemble learning tool for compound classification and QSAR modeling. J Chem Inf Model 45(3):786–799
Tetkoa IV, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P, Palyulin VA, Radchenko EV, Zefirov NS, Makarenko AS, Tanchuk VY, Prokopenko VV (2005) Virtual computational chemistry laboratory design and description. J Comput Aided Mol Des 19(6):453–463
Tsang ECC, Chen D, Yeung DS, Wang XZ, Lee J (2008) Attributes reduction using fuzzy rough sets. IEEE Trans Fuzzy Syst 16(5):1130–1141
Tuppurainen K, Viisas M, Laatikainen R, Peräkylä M (2002) Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set. J Chem Inf Comput Sci 42(3):607–613
Turner DB, Willett P, Ferguson AM, Heritage TW (1999) Evaluation of a novel molecular vibration-based descriptor (EVA) for QSAR studies: 2. model validation using a benchmark steroid dataset. J Comput Aided Mol Des 13(3):271–296
Uddameri V, Kuchanur M (2004) Fuzzy QSARs for predicting log \(K_{oc}\) of persistent organic pollutants. Chemosphere 54(6):771–776
Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York
Wroblewski J (1995) Finding minimal reducts using genetic algorithms. In: Proceedings of the 2nd annual joint conference on information sciences, pp 186–189
Wu H, Wu Y, Luo J (2009) An interval type-2 fuzzy rough set model for attribute reduction. IEEE Trans Fuzzy Syst 17(2):301–315
Yamaguchi D (2009) Attribute dependency functions considering data efficiency. Int J Approximate Reasoning 51:89–98
Zhong N, Dong J, Ohsuga S (2001) Using rough sets with heuristics for feature selection. J Intell Inf Syst 16:199–214
Zhou YP, Cai CB, Huan S, Jiang JH, Wu HL, Shen GL, Yu RQ (2007) QSAR study of angiotensin II antagonists using robust boosting partial least squares regression. Anal Chim Acta 593:68–74
Ziarko W (1993) Variable precision rough set model. J Comput Syst Sci 46:39–59
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Maji, P., Paul, S. (2014). Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules. In: Scalable Pattern Recognition Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-05630-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-05630-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05629-6
Online ISBN: 978-3-319-05630-2
eBook Packages: Computer ScienceComputer Science (R0)