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An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Izrailev, S., & Agrafiotis, D. (2001). A novel method for building regression tree models for QSAR
based on artificial ant colony systems. Journal of Chemical Information and Computer Sciences, 41(1),
176–180. doi:10.1021/ci000336s PMID:11206370
Izrailev, S., & Agrafiotis, D. K. (2002). Variable selection for QSAR by artificial ant colony systems.
SAR and QSAR in Environmental Research, 13(3-4), 417–423. doi:10.1080/10629360290014296
PMID:12184383
Jackson, C., Esnouf, M. P., Winzor, D., & Duewer, D. (2007). Defining and measuring biological
activity: Applying the principles of metrology. Accreditation and Quality Assurance, 12(6), 283–294.
doi:10.1007/s00769-006-0254-1
Jalali-Heravi, M., & Asadollahi-Baboli, M. (2009). Quantitative structure-activity relationship study of
serotonin (5-HT7) receptor inhibitors using modified ant colony algorithm and adaptive neuro-fuzzy interference system (ANFIS). European Journal of Medicinal Chemistry, 44(4), 1463–1470. doi:10.1016/j.
ejmech.2008.09.050 PMID:19013691
Jimenez Villalobos, T. P., Gaitan Ibarra, R., & Montalvo Acosta, J. J. (2013). 2D, 3D-QSAR and molecular
docking of 4(1H)-quinolones analogues with antimalarial activities. Journal of Molecular Graphics &
Modelling, 46, 105–124. doi:10.1016/j.jmgm.2013.10.002 PMID:24185373
Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York: Springer.
Jores-Kong, H., & Wold, H. (1982). Systems under indirect observation: Causality, structure, prediction.
Amsterdam, The Netherlands: North-Holland.
Kadam, R. U., & Roy, N. (2006). Cluster analysis and two-dimensional quantitative structure-activity
relationship (2D-QSAR) of Pseudomonas aeruginosa deacetylase LpxC inhibitors. Bioorganic & Me-
dicinal Chemistry Letters, 16(19), 5136–5143. doi:10.1016/j.bmcl.2006.07.041 PMID:16879960
Karelson, M., Lobanov, V. S., & Katritzky, A. R. (1996). Quantum-chemical descriptors in QSAR/QSPR
studies. Chemical Reviews, 96(3), 1027–1044. doi:10.1021/cr950202r PMID:11848779
Katritzky, A. R., Dobchev, D. A., Fara, D. C., Hur, E., Tamm, K., & Kurunczi, L. etal. (2006). Skin
permeation rate as a function of chemical structure. Journal of Medicinal Chemistry, 49(11), 3305–3314.
doi:10.1021/jm051031d PMID:16722649
Katritzky, A. R., & Gordeeva, E. V. (1993). Traditional topological indices vs electronic, geometrical,
and combined molecular descriptors in QSAR/QSPR research. J Journal of Chemical Information and
Computer Sciences, 33(6), 835–857. PMID:8113335
Katritzky, A. R., Kuanar, M., Fara, D. C., Karelson, M., Acree, W. E. Jr, Solov’ev, V. P., & Varnek, A.
(2005). QSAR modeling of blood:air and tissue:air partition coefficients using theoretical descriptors.
Bioorganic & Medicinal Chemistry, 13(23), 6450–6463. doi:10.1016/j.bmc.2005.06.066 PMID:16202613
Katritzky, A. R., Kuanar, M., Slavov, S., Dobchev, D. A., Fara, D. C., & Karelson, M. etal. (2006).
Correlation of blood-brain penetration using structural descriptors. Bioorganic & Medicinal Chemistry,
14(14), 4888–4917. doi:10.1016/j.bmc.2006.03.012 PMID:16697202
36
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Katritzky, A. R., Lobanov, V. S., & Karelson, M. (1995). QSPR: The correlation and quantitative prediction of chemical and physical properties from structure. Chemical Society Reviews, 24(4), 279–287.
doi:10.1039/cs9952400279
Katritzky, A. R., Lobanov, V. S., & Karelson, M. (2007). Comprehensive descriptors for structural and
statistical analysis. CODESSA.
Katritzkya, A. R., Slavova, S. H., Dobcheva, D. A., & Karelson, M. (2007). Comparison between 2D
and 3D-QSAR approaches to correlate inhibitor activity for a series of indole amide hydroxamic acids.
QSAR & Combinatorial Science, 26(3), 333–345. doi:10.1002/qsar.200630021
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Paper presented at the IEEE Interna-
tional Conference on Neural Networks. doi:10.1109/ICNN.1995.488968
Kier, L. B., & Hall, L. H. (1986). Molecular connectivity in structure-activity analysis. New York: Wiley.
Kireeva, D. B. (1995). ChemNet: A novel neural network based method for graph/property mapping.
Journal of Chemical Information and Computer Sciences, 35, 175–180.
Klebe, G., Abraham, U., & Mietzner, T. (1994). Molecular similarity indices in a comparative analysis
(CoMSIA) of drug molecules to correlate and predict their biological activity. Journal of Medicinal
Chemistry, 37(24), 4130–4146. doi:10.1021/jm00050a010 PMID:7990113
Koga, H., Itoh, A., Murayama, S., Suzue, S., & Irikura, T. (1980). Structure-activity relationships of
antibacterial 6,7- and 7,8-disubstituted 1-alkyl-1,4-dihydro-4-oxoquinoline-3-carboxylic acids. Journal
of Medicinal Chemistry, 23(12), 1358–1363. doi:10.1021/jm00186a014 PMID:7452690
Kubinyi, H. (1993). QSAR: Hansch analysis and related approaches. Weinheim: VCH.
doi:10.1002/9783527616824
Leach, A. R., & Gillet, V. J. (2007). An introduction to cheminformatics. Dordrecht: Springer.
doi:10.1007/978-1-4020-6291-9
Leonard, J. T., & Roy, K. (2006). QSAR by LFER model of HIV protease inhibitor mannitol derivatives
using FA-MLR, PCRA, and PLS techniques. Bioorganic & Medicinal Chemistry, 14(4), 1039–1046.
doi:10.1016/j.bmc.2005.09.022 PMID:16213730
Li, B. K., Cong, Y., Yang, X. G., Xue, Y., & Chen, Y. Z. (2013). In silico prediction of spleen tyrosine
kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method. Computers in Biology and Medicine, 43(4), 395–404.
doi:10.1016/j.compbiomed.2013.01.015 PMID:23402937
Li, G.-Z., & Zeng, X.-Q. (2009). Feature selection for partial least square based dimension reduction.
Studies in Computational Intelligence, 205, 3–37. doi:10.1007/978-3-642-01536-6_1
Liew, C. Y., & Yap, C. W. (2012). Current modeling methods used in QSAR/QSPR. In M. Dehmer,
K. Varmuza, & D. Bonchev (Eds.), Statistical modelling of molcular descriptors in QSAR/QSPR (pp.
1–31). Wiley.
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
37
An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Lin, T. H., Li, H. T., & Tsai, K. C. (2004). Implementing the Fisher’s discriminant ratio in a k-means
clustering algorithm for feature selection and data set trimming. Journal of Chemical Information and
Computer Sciences, 44(1), 76–87. doi:10.1021/ci030295a PMID:14741013
Liu, P., & Long, W. (2009). Current mathematical methods used in QSAR/QSPR studies. International
Journal of Molecular Sciences, 10(5), 1978–1998. doi:10.3390/ijms10051978 PMID:19564933
Livingstone, D. J., & Salt, D. W. (2005). Judging the significance of multiple linear regression models.
Journal of Medicinal Chemistry, 48(3), 661–663. doi:10.1021/jm049111p PMID:15689150
Lozano, N. B., Maltarollo, V. G., Weber, K. C., Honorio, K. M., Guido, R. V., Andricopulo, A. D., &
Da Silva, A. B. (2012). Molecular features for antitrypanosomal activity of thiosemicarbazones revealed
by OPS-PLS QSAR studies. Medicinal Chemistry (Shariqah, United Arab Emirates), 8(6), 1045–1056.
PMID:22779790
Lu, J. X., Shen, Q., Jiang, J. H., Shen, G. L., & Yu, R. Q. (2004). QSAR analysis of cyclooxygenase
inhibitor using particle swarm optimization and multiple linear regression. Journal of Pharmaceutical
and Biomedical Analysis, 35(4), 679–687. doi:10.1016/j.jpba.2004.02.026 PMID:15193712
Lushington, G. H., Guo, J. X., & Wang, J. L. (2007). Whither combine? New opportunities for receptorbased QSAR. Current Medicinal Chemistry, 14(17), 1863–1877. doi:10.2174/092986707781058814
PMID:17627522
Maccari, L., Magnani, M., Strappaghetti, G., Corelli, F., Botta, M., & Manetti, F. (2006). A geneticfunction-approximation-based QSAR model for the affinity of arylpiperazines toward alpha1 adrenoceptors. Journal of Chemical Information and Modeling, 46(3), 1466–1478. doi:10.1021/ci060031z
PMID:16711766
Martinez, A. M., & Kak, A. C. (2001). PCA versus LDA. IEEE Transactions on Pattern Analysis and
Machine Intelligence, 23(2), 228–233. doi:10.1109/34.908974
Martins, J. P., Barbosa, E. G., Pasqualoto, K. F., & Ferreira, M. M. (2009). LQTA-QSAR: A new 4DQSAR methodology. Journal of Chemical Information and Modeling, 49(6), 1428–1436. doi:10.1021/
ci900014f PMID:19422246
Mc, C. W., & Pitts, W. (1948). The statistical organization of nervous activity. Biometrics, 4(2), 91–99.
doi:10.2307/3001453 PMID:18871168
Mercader, A. G., Duchowicz, P. R., Fernández, F. M., & Castro, E. A. (2008). Modified and enhanced
replacement method for the selection of molecular descriptors in QSAR and QSPR theories. Chemomet-
rics and Intelligent Laboratory Systems, 92(2), 138–144. doi:10.1016/j.chemolab.2008.02.005
Mercader, A. G., Duchowicz, P. R., Fernandez, F. M., & Castro, E. A. (2010). Replacement method and
enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories. Journal of Chemical Information and Modeling, 50(9), 1542–1548.
doi:10.1021/ci100103r PMID:20722426
Mercader, A. G., Duchowicz, P. R., Fernandez, F. M., & Castro, E. A. (2011). Advances in the replacement and enhanced replacement method in QSAR and QSPR theories. Journal of Chemical Information
and Modeling, 51(7), 1575–1581. doi:10.1021/ci200079b PMID:21644502
38
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Moody, J., & Darken, C. (1989). Fast learning in networks of locally-tuned processing units. Neural
Computation, 1(2), 281–294. doi:10.1162/neco.1989.1.2.281
Morris, G. M., Goodsell, D. S., Huey, R., & Olson, A. J. (1996). Distributed automated docking
of flexible ligands to proteins: Parallel applications of AutoDock 2.4. Journal of Computer-Aided
Molecular Design, 10(4), 293–304. doi:10.1007/BF00124499 PMID:8877701
Mozrzymas, A. (2013). Modelling of the critical micelle concentration of cationic gemini surfactants
using molecular connectivity indices. Journal of Solution Chemistry, 42(11), 2187–2199. doi:10.1007/
s10953-013-0095-6 PMID:24319301
Munteanu, C. R., Fernandez-Blanco, E., Seoane, J. A., Izquierdo-Novo, P., Rodriguez-Fernandez,
J. A., & Prieto-Gonzalez, J. M. et al. (2010). Drug discovery and design for complex diseases
through QSAR computational methods. Current Pharmaceutical Design, 16(24), 2640–2655.
doi:10.2174/138161210792389252 PMID:20642425
Myint, K. Z., & Xie, X. Q. (2010). Recent advances in fragment-based QSAR and multi-dimensional
QSAR methods. International Journal of Molecular Sciences, 11(10), 3846–3866. doi:10.3390/
ijms11103846 PMID:21152304
Netzeva, T. I., Pavan, M., & Worth, A. P. (2008). Review of (quantitative) structure–activity relationships for acute aquatic toxicity. QSAR & Combinatorial Science, 27(1), 77–90. doi:10.1002/
qsar.200710099
Noorizadeh, H., Sajjadifar, S., & Farmany, A. (2013). A quantitative structure-activity relationship
study of anti-HIV activity of substituted HEPT using nonlinear models. Medicinal Chemistry Re-
search, 22(11), 5442–5452. doi:10.1007/s00044-013-0525-4 PMID:24098069
Oberdorf, C., Schmidt, T. J., & Wunsch, B. (2010). 5D-QSAR for spirocyclic sigma1 receptor ligands
by Quasar receptor surface modeling. European Journal of Medicinal Chemistry, 45(7), 3116–3124.
doi:10.1016/j.ejmech.2010.03.048 PMID:20427100
Olah, M., Bologa, C., & Oprea, T. I. (2004). An automated PLS search for biologically relevant
QSAR descriptors. Journal of Computer-Aided Molecular Design, 18(7-9), 437–449. doi:10.1007/
s10822-004-4060-8 PMID:15729845
Oliveira Junior, H. A. e. (2012). Stochastic global optimization and its applications with fuzzy adap-
tive simulated annealing. New York: Springer-Verlag. doi:10.1007/978-3-642-27479-4
Ou, Y. Y., Gromiha, M. M., Chen, S. A., & Suwa, M. (2008). TMBETADISC-RBF: Discrimination
of beta-barrel membrane proteins using RBF networks and PSSM profiles. Computational Biology
and Chemistry, 32(3), 227–231. doi:10.1016/j.compbiolchem.2008.03.002 PMID:18434251
Ou-Yang, S. S., Lu, J. Y., Kong, X. Q., Liang, Z. J., Luo, C., & Jiang, H. (2012). Computational drug
discovery. Acta Pharmacologica Sinica, 33(9), 1131–1140. doi:10.1038/aps.2012.109 PMID:22922346
Panagiotou, G., & Taboureau, O. (2012). The impact of network biology in pharmacology and
toxicology. SAR and QSAR in Environmental Research, 23(3-4), 221–235. doi:10.1080/106293
6X.2012.657237 PMID:22352466
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
39
An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Pastor, M., Cruciani, G., McLay, I., Pickett, S., & Clementi, S. (2000). GRid-INdependent descriptors
(GRIND): A novel class of alignment-independent three-dimensional molecular descriptors. Journal of
Medicinal Chemistry, 43(17), 3233–3243. doi:10.1021/jm000941m PMID:10966742
Patil, D., Raj, R., Shingade, P., Kulkarni, B., & Jayaraman, V. K. (2009). Feature selection and classification employing hybrid ant colony optimization/random forest methodology. Combinatorial Chemistry
& High Throughput Screening, 12(5), 507–513. doi:10.2174/138620709788488993 PMID:19519330
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Maga-
zine, 2(11), 559–572. doi:10.1080/14786440109462720
Perez-Castillo, Y., Lazar, C., Taminau, J., Froeyen, M., Cabrera-Perez, M. A., & Nowe, A. (2012). GA(M)
E-QSAR: A novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design. Journal of Chemical Information and Modeling, 52(9), 2366–2386.
doi:10.1021/ci300146h PMID:22856471
Peristera, O., Spreafico, M., Smiesko, M., Ernst, B., & Vedani, A. (2009). Mixed-model QSAR at the
human mineralocorticoid receptor: Predicting binding mode and affinity of anabolic steroids. Toxicology
Letters, 189(3), 219–224. doi:10.1016/j.toxlet.2009.05.025 PMID:19523507
Pitts, W., & Mc, C. W. (1947). How we know universals: The perception of auditory and visual forms.
The Bulletin of Mathematical Biophysics, 9(3), 127–147. doi:10.1007/BF02478291 PMID:20262674
Polanski, J. (2009). Receptor dependent multidimensional QSAR for modeling drug-receptor interactions.
Current Medicinal Chemistry, 16(25), 3243–3257. doi:10.2174/092986709788803286 PMID:19548875
Polanski, J., Gieleciak, R., & Bak, A. (2002). The comparative molecular surface analysis (COMSA)--a
nongrid 3D QSAR method by a coupled neural network and PLS system: Predicting pK(a) values of
benzoic and alkanoic acids. Journal of Chemical Information and Computer Sciences, 42(2), 184–191.
doi:10.1021/ci010031t PMID:11911685
Polanski, J., & Walczak, B. (2000). The comparative molecular surface analysis (COMSA): A novel tool
for molecular design. Computers & Chemistry, 24(5), 615–625. doi:10.1016/S0097-8485(00)00064-4
PMID:10890372
Pourbasheer, E., Riahi, S., Ganjali, M. R., & Norouzi, P. (2010). Quantitative structure-activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the
genetic algorithm and multiple linear regression (GA-MLR) method. Journal of Enzyme Inhibition and
Medicinal Chemistry, 25(6), 844–853. doi:10.3109/14756361003757893 PMID:20429783
Prakash, O., & Khan, F. (2013). Cluster based SVR-QSAR modelling for HTS records: An implementation for anticancer leads against human breast cancer. Combinatorial Chemistry & High Throughput
Screening, 16(7), 511–521. doi:10.2174/1386207311316070002 PMID:23488974
Raichurkar, A. V., Shah, U. A., & Kulkarni, V. M. (2011). 3D-QSAR of novel phosphodiesterase-4
inhibitors by genetic function approximation. Medicinal Chemistry (Shariqah, United Arab Emirates),
7(6), 543–552. doi:10.2174/157340611797928352 PMID:22313294
40
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Randic, M. (2001). The connectivity index 25 years after. Journal of Molecular Graphics & Modelling,
20(1), 19–35. doi:10.1016/S1093-3263(01)00098-5 PMID:11760000
Rekker, R. F., & Mannhold, R. (1992). Calculation of drug lipophilicity: The hydrophobic fragmental
constant approach (Vol. 12). Weinheim: VCH.
Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms.
Academic Press.
Roy, K., & Pratim Roy, P. (2009). Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS
and ANN techniques. European Journal of Medicinal Chemistry, 44(7), 2913–2922. doi:10.1016/j.
ejmech.2008.12.004 PMID:19128860
Sahin, K., Saripinar, E., Yanmaz, E., & Gecen, N. (2011). Quantitative bioactivity prediction and
pharmacophore identification for benzotriazine derivatives using the electron conformational-genetic
algorithm in QSAR. SAR and QSAR in Environmental Research, 22(3), 217–238. doi:10.1080/106293
6X.2010.548341 PMID:21391137
Sapre, N. S., Pancholi, N., Gupta, S., & Sapre, N. (2008). Computational modeling of tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone derivatives: An atomistic drug design approach using Kier-Hall
electrotopological state (E-state) indices. Journal of Computational Chemistry, 29(11), 1699–1706.
doi:10.1002/jcc.20931 PMID:18351618
Scior, T., Medina-Franco, J. L., Do, Q. T., Martinez-Mayorga, K., Yunes Rojas, J. A., & Bernard, P.
(2009). How to recognize and workaround pitfalls in QSAR studies: A critical review. Current Medicinal
Chemistry, 16(32), 4297–4313. doi:10.2174/092986709789578213 PMID:19754417
Shamsipur, M., Zare-Shahabadi, V., Hemmateenejad, B., & Akhond, M. (2009). An efficient variable
selection method based on the use of external memory in ant colony optimization. Application to QSAR/
QSPR studies. Analytica Chimica Acta, 646(1-2), 39–46. doi:10.1016/j.aca.2009.05.005 PMID:19523554
Shen, M., Beguin, C., Golbraikh, A., Stables, J. P., Kohn, H., & Tropsha, A. (2004). Application of
predictive QSAR models to database mining: Identification and experimental validation of novel anticonvulsant compounds. Journal of Medicinal Chemistry, 47(9), 2356–2364. doi:10.1021/jm030584q
PMID:15084134
Shen, Q., Jiang, J. H., Tao, J. C., Shen, G. L., & Yu, R. Q. (2005). Modified ant colony optimization
algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors. Journal
of Chemical Information and Modeling, 45(4), 1024–1029. doi:10.1021/ci049610z PMID:16045297
Shi, W. M., Shen, Q., Kong, W., & Ye, B. X. (2007). QSAR analysis of tyrosine kinase inhibitor using modified ant colony optimization and multiple linear regression. European Journal of Medicinal
Chemistry, 42(1), 81–86. doi:10.1016/j.ejmech.2006.08.001 PMID:16996653
Silverman, B. D., & Platt, D. E. (1996). Comparative molecular moment analysis (CoMMA): 3D-QSAR
without molecular superposition. Journal of Medicinal Chemistry, 39(11), 2129–2140. doi:10.1021/
jm950589q PMID:8667357
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
41
An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Sullivan, K. M., Manuppello, J. R., & Willett, C. E. (2014). Building on a solid foundation: SAR and
QSAR as a fundamental strategy to reduce animal testing. SAR and QSAR in Environmental Research,
1–9. doi:10.1080/1062936x.2014.907203 PMID:24773450
Sun, M., Zheng, Y., Wei, H., Chen, J., Cai, J., & Ji, M. (2009). Enhanced replacement method-based
quantitative structure–activity relationship modeling and support vector machine classification of 4-anilino-3-quinolinecarbonitriles as src kinase inhibitors. QSAR & Combinatorial Science, 28(3), 312–324.
doi:10.1002/qsar.200860107
Sun, M., Zheng, Y., Wei, H., Chen, J., & Ji, M. (2009). QSAR studies on 4-anilino-3-quinolinecarbonitriles
as Src kinase inhibitors using robust PCA and both linear and nonlinear models. Journal of Enzyme Inhi-
bition and Medicinal Chemistry, 24(5), 1109–1116. doi:10.1080/14756360802632906 PMID:19555174
Sun, Y., Todorovic, S., & Goodison, S. (2010). Local-learning-based feature selection for high-dimensional
data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1610–1626.
doi:10.1109/TPAMI.2009.190 PMID:20634556
Taft, R. W. Jr. (1952). Polar and steric substituent constants for aliphatic and o-benzoate groups from
rates of esterification and hydrolysis of esters1. Journal of the American Chemical Society, 74(12),
3120–3128. doi:10.1021/ja01132a049
Tiwari, A., Saxena, S., Pant, A. B., & Srivastava, P. (2012). Protein-ligand interaction studies of retinolbinding protein 3 with herbal molecules using AutoDock for the management of Eales’ disease. Journal
of Ocular Biology, Diseases, and Informatics, 5(2), 40-43. doi: 10.1007/s12177-012-9098-6
Todeschini, R., & Consonni, V. (2000). Handbook of molecular descriptors (Vol. 11). Weinheim: WILEY-
VCH Verlag GmbH. doi:10.1002/9783527613106
Topliss, J. (1993). Some observations on classical QSAR. Perspectives in Drug Discovery and Design,
1(2), 253-268.
Toropov, A. A., Toropova, A. P., Raska, I. Jr, Leszczynska, D., & Leszczynski, J. (2014). Comprehension
of drug toxicity: Software and databases. Computers in Biology and Medicine, 45, 20–25. doi:10.1016/j.
compbiomed.2013.11.013 PMID:24480159
Tropsha, A. (2010). Best practices for QSAR model development, validation, and exploitation. Molecular
Informatics, 29(6-7), 476–488. doi:10.1002/minf.201000061
Tropsha, A., Gramatica, P., & Gombar, V. K. (2003). The importance of being earnest: Validation is the
absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial
Science, 22(1), 69–77. doi:10.1002/qsar.200390007
Vedani, A., Descloux, A. V., Spreafico, M., & Ernst, B. (2007). Predicting the toxic potential of drugs and
chemicals in silico: A model for the peroxisome proliferator-activated receptor gamma (PPAR gamma).
Toxicology Letters, 173(1), 17–23. doi:10.1016/j.toxlet.2007.06.011 PMID:17643875
Vedani, A., & Dobler, M. (2002). 5D-QSAR: The key for simulating induced fit? Journal of Medicinal
Chemistry, 45(11), 2139–2149. doi:10.1021/jm011005p PMID:12014952
42
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Vedani, A., Dobler, M., Dollinger, H., Hasselbach, K. M., Birke, F., & Lill, M. A. (2005). Novel ligands
for the chemokine receptor-3 (CCR3): A receptor-modeling study based on 5D-QSAR. Journal of Me-
dicinal Chemistry, 48(5), 1515–1527. doi:10.1021/jm040827u PMID:15743194
Vedani, A., Dobler, M., & Lill, M. A. (2005). Combining protein modeling and 6D-QSAR. Simulating
the binding of structurally diverse ligands to the estrogen receptor. Journal of Medicinal Chemistry,
48(11), 3700–3703. doi:10.1021/jm050185q PMID:15916421
Vedani, A., Dobler, M., & Lill, M. A. (2006). The challenge of predicting drug toxicity in silico. Basic
& Clinical Pharmacology & Toxicology, 99(3), 195–208. doi:10.1111/j.1742-7843.2006.pto_471.x
PMID:16930291
Vera-Divaio, M. A., Freitas, A. C., Castro, H. C., de Albuquerque, S., Cabral, L. M., & Rodrigues, C. R.
etal. (2009). Synthesis, antichagasic in vitro evaluation, cytotoxicity assays, molecular modeling and SAR/
QSAR studies of a 2-phenyl-3-(1-phenyl-1H-pyrazol-4-yl)-acrylic acid benzylidene-carbohydrazide series.
Bioorganic & Medicinal Chemistry, 17(1), 295–302. doi:10.1016/j.bmc.2008.10.085 PMID:19036592
Verloop, A. (1987). The STERIMOL approach to drug design. New York: Marcel Dekker.
Verma, J., Khedkar, V. M., & Coutinho, E. C. (2010). 3D-QSAR in drug design--A review. Current
Topics in Medicinal Chemistry, 10(1), 95–115. doi:10.2174/156802610790232260 PMID:19929826
von Itzstein, M., Wu, W. Y., Kok, G. B., Pegg, M. S., Dyason, J. C., & Jin, B. etal. (1993). Rational
design of potent sialidase-based inhibitors of influenza virus replication. Nature, 363(6428), 418–423.
doi:10.1038/363418a0 PMID:8502295
Walters, D. E., & Hinds, R. M. (1994). Genetically evolved receptor models: A computational approach
to construction of receptor models. Journal of Medicinal Chemistry, 37(16), 2527–2536. doi:10.1021/
jm00042a006 PMID:8057298
Wang, L., Liu, X., Shan, Z., & Shi, L. (2010). Using electrotopological state indices to model the depuration rates of polychlorinated biphenyls in mussels of Elliptio complanata. Journal of Environmental
Sciences (China), 22(10), 1544–1550. doi:10.1016/S1001-0742(09)60287-4 PMID:21235184
Wang, R., Jiang, J., Pan, Y., Cao, H., & Cui, Y. (2009). Prediction of impact sensitivity of nitro energetic
compounds by neural network based on electrotopological-state indices. Journal of Hazardous Materi-
als, 166(1), 155–186. doi:10.1016/j.jhazmat.2008.11.005 PMID:19101083
Warr, W. A. (2011). Representation of chemical structures. Advanced Review, 1, 557–579.
Weininger, D. (1988). SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 28(1), 31–36.
doi:10.1021/ci00057a005
Weininger, D., Weininger, A., & Weininger, J. L. (1989). SMILES. 2. Algorithm for generation of
unique SMILES notation. Journal of Chemical Information and Computer Sciences, 29(2), 97–101.
doi:10.1021/ci00062a008
Wiener, H. (1947). Structural determination of paraffin boiling points. Journal of the American Chemi-
cal Society, 69(1), 17–20. doi:10.1021/ja01193a005 PMID:20291038
EBSCOhost - printed on 2/14/2023 7:16 AM via . All use subject to https://www.ebsco.com/terms-of-use
43
An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Williams, M. S., Weiss, E. J., Sabatine, M. S., Simon, D. I., Bahou, W. F., & Becker, L. C. etal. (2010).
Genetic regulation of platelet receptor expression and function: Application in clinical practice and drug
development. Arteriosclerosis, Thrombosis, and Vascular Biology, 30(12), 2372–2384. doi:10.1161/
ATVBAHA.110.218131 PMID:21084706
Wold, H. (1966). Research papers in statistics. New York, NY: Wiley.
Wold, S., & Eriksson, L. (1995). Statistical validation of QSAR results. In H. Waterbeemd (Ed.), Chemomet-
ric methods in molecular design (Vol. 2, pp. 309–318). Weinheim: VCH. doi:10.1002/9783527615452.ch5
Xia, B., Liu, K., Gong, Z., Zheng, B., Zhang, X., & Fan, B. (2009). Rapid toxicity prediction of organic
chemicals to Chlorella vulgaris using quantitative structure-activity relationships methods. Ecotoxicol-
ogy and Environmental Safety, 72(3), 787–794. doi:10.1016/j.ecoenv.2008.09.002 PMID:18950860
Young, D. C. (2009). Computational drug design: A guide for computational and medicinal chemists.
John Wiely & Sons. doi:10.1002/9780470451854
Yuan, H., & Parrill, A. (2005). Cluster analysis and three-dimensional QSAR studies of HIV-1 integrase
inhibitors. Journal of Molecular Graphics & Modelling, 23(4), 317–328. doi:10.1016/j.jmgm.2004.10.003
PMID:15670952
Zarei, K., Atabati, M., & Kor, K. (2014). Bee algorithm and adaptive neuro-fuzzy inference system as
tools for QSAR study toxicity of substituted benzenes to tetrahymena pyriformis. Bulletin of Environmen-
tal Contamination and Toxicology, 92(6), 642–649. doi:10.1007/s00128-014-1253-2 PMID:24638918
Zhang, J., Rivard, B., & Rogge, D. M. (2008). The successive projection algorithm (SPA), an algorithm
with a spatial constraint for the automatic search of endmembers in hyperspectral data. Sensors (Basel,
Switzerland), 8(2), 1321–1342. doi:10.3390/s8021321
Zhang, S., Wei, L., Bastow, K., Zheng, W., Brossi, A., Lee, K. H., & Tropsha, A. (2007). Antitumor
agents 252. Application of validated QSAR models to database mining: Discovery of novel tylophorine derivatives as potential anticancer agents. Journal of Computer-Aided Molecular Design, 21(1-3),
97–112. doi:10.1007/s10822-007-9102-6 PMID:17340042
Zhu, H. (2013). From QSAR to QSIIR: Searching for enhanced computational toxicology models. Methods
in Molecular Biology (Clifton, N.J.), 930, 53–65. doi:10.1007/978-1-62703-059-5_3 PMID:23086837
KEY TERMS AND DEFINITIONS
3D-QSAR: Quantitative analysis of relationship between spatial features of a set of compounds and
biological activities using statistical methods.
Accuracy: The accuracy of a predicted model, the most important quality measurement, is usually
calculated for both training and test sets.
Dimensionality Reduction: A transformation function/algorithm which maps a large dataset of
descriptors in to a small dataset of descriptors as a representative subset of the large dataset. The algorithms can be either linear or non-linear.
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An Introduction to the Basic Concepts in QSAR-Aided Drug Design
Machine Learning: An important field of artificial intelligence in computer science is the machine
learning. Machine learning algorithms are diverse and are used to extract the rules of black box functionality
by mapping the inputs to the corresponding outputs using the computational and statistical calculations.
Molecular Descriptor: Numerical representation of molecular properties (e.g. physicochemical
properties) of chemical structures.
Pharmacophore: Minimum structural requirement of a compound necessary for interaction with a
target molecule.
QSAR: Quantitative correlation between biological activities of compounds and chemical structures
represented by a regression model.
Test Data Set: A set of compounds which does not have any role in training process and is used for
model evaluation.
Training Data Set: A set of molecules selected for training of the model using machine learning
algorithms.
ENDNOTES
1
Tripos is a well-known and global pioneer in computational drug discovery capabilities by design-
ing and development of scientific software products. http://www.tripos.com
2
Advanced Chemistry Development, Inc., (ACD/Labs) is one of the companies providing chemistry
software for data management and informatics. ACD/Labs suite of programs provide in silico pre-
diction of molecular physical properties and ADMET property. ACD/Labs Release 2014 (v2014)
software is the latest version.http://www.acdlabs.com/
3
CambridgeSoft was a cheminformatics software company. In 2011, the company was sold to
PerkinElmer, a laboratory instruments company. The collection of programs supplies the chemical
structures and their associated data and properties.http://www.cambridgesoft.com/
4
InfoChem is a collection of software tools and applications for chemical data management, stor-
age, handling and retrieval of structures and reactions.http://www.infochem.de/
5
Dragon was developed by an Italian company, namely, Talete. It is designed for calculation of
molecular descriptors which can be used in QSAR studies. The latest version is Dragon 6. It can
be found at:http://www.talete.mi.it/products/dragon_description.htm
6
PubChem was released in 2004 as a database within national center for biotechnology information
(NCBI) for providing information on chemical structure and biological activities of small molecules.
7
ChemDraw is a chemical drawing tool for generating images of manuscripts, databases, and mak-
ing useful queries in chemical databases
https://www.cambridgesoft.com/Ensemble_for_Chemistry/ChemDraw/
8
ACD/ChemSketch is a drawing package that includes drawing and calculation of chemical struc-
tures and molecular properties, and prediction of logP.
http://www.acdlabs.com/resources/freeware/chemsketch/
9
Open Babel is a free chemical toolbox designed to search, convert, analyze, or store data from
molecular modeling and related areas. http://openbabel.org/wiki/Main_Page
10
CORINA is useful in generating 3D structure for small and medium sized molecules.
https://www.molecular-networks.com/products/corina
11
Concord which is included in SYBLE is used for equality assessments on every structures in 3D.
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