The review of the most used computational methods for studies of the relationships between molecular structure and biological activity
Keywords:QSAR, drug development, molecular modeling, QSAR methods, mathematical models, variable selection methods, machine learning
Aim. To systematize the most used methods of “molecular structure-biological activity” relationship studies and to disclose their principles of application, strong and weak sides.
Methods. The review of modern scientific literature devoted to QSAR modeling was carried out. The most frequently used methods for “structure-activity” models development were selected for further description.
Results. The place of “molecular structure-activity” relationships analysis among computer assisted drug design methods is discussed in the current review and the most used algorithms of QSAR model development with emphasis on the mechanisms of their work are described. The approaches based on model ensembles become more and more popular, one of which is Random Forest.
Conclusions. The progress in machine learning methods development is the key to the further evolution of QSAR direction and to the discovering of new biologically active substances
Dudek, A., Arodz, T., Galvez, J. (2006). Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review. Combinatorial Chemistry & High Throughput Screening, 9 (3), 213–228. doi: 10.2174/138620706776055539
Arakawa, M., Hasegawa, K., Funatsu, K. (2007). The Recent Trend in QSAR Modeling – Variable Selection and 3D-QSAR Methods. Current Computer Aided-Drug Design, 3 (4), 254–262. doi: 10.2174/1573409077827994177
Tropsha, A., Golbraikh, A. (2007). Predictive QSAR Modeling Workflow, Model Applicability Domains, and Virtual Screening. Current Pharmaceutical Design, 13 (34), 3494–3504. doi: 10.2174/138161207782794257
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M. et. al (2014). QSAR Modeling: Where Have You Been? Where Are You Going To? Journal of Medicinal Chemistry, 57 (12), 4977–5010. doi: 10.1021/jm4004285
Devinyak, O. T., Slivka, M. V., Slivka, M. V., Vais, V. M., Lendel, V. G. (2011). Quantitative structure-activity relationship study and directed synthesis of Thieno[2,3-d]pyrimidine-2,4-diones as monocarboxylate transporter 1 inhibitors. Medicinal Chemistry Research, 21 (9), 2263–2272. doi: 10.1007/s00044-011-9748-4
Devinyak, O. T., Havrylyuk, D.Y., Zimenkovsky, B. S., Lesyk, R. B. (2011). QSAR analiz 2(4)-tiazolidynoniv iz pirazolinovym frahmentom v molekulakh, shcho proiavliaiut protypukhlynnu aktyvnist shchodo klityn nedribnoklitynnoho raku lehen in vitro [QSAR Study of 2(4)-Thiazolidinones with Pyrazoline Scaffold Possesing Antitumor Activity in vitro on Nonsmall Cell Lung Cancer Cells]. Clinical Pharmacy, Pharmacotherapy and Medical Standardization, 3-4, 163–168.
Li, Y.-W., Li, B., He, J., Qian, P. (2011). Structure-activity relationship study of antioxidative peptides by QSAR modeling: the amino acid next to C-terminus affects the activity. Journal of Peptide Science, 17 (6), 454–462. doi: 10.1002/psc.1345
Mazanetz, M. P., Ichihara, O., Law, R. J., Whittaker, M. (2011). Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method. Journal of Cheminformatics, 3 (1), 2. doi: 10.1186/1758-2946-3-2
Suenderhauf, C., Hammann, F., Maunz, A., Helma, C., Huwyler, J. (2011). Combinatorial QSAR Modeling of Human Intestinal Absorption. Molecular Pharmaceutics, 8 (1), 213–224. doi: 10.1021/mp100279d
Sun, H. (2005). A Naive Bayes Classifier for Prediction of Multidrug Resistance Reversal Activity on the Basis of Atom Typing. Journal of Medicinal Chemistry, 48 (12), 4031–4039. doi: 10.1021/jm050180t
Prathipati, P., Ma, N. L., Keller, T. H. (2008). Global Bayesian Models for the Prioritization of Antitubercular Agents. Journal of Chemical Information and Modeling, 48 (12), 2362–2370. doi: 10.1021/ci800143n
Lv, W., Xue, Y. (2010). Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods. European Journal of Medicinal Chemistry, 45 (3), 1167–1172. doi: 10.1016/j.ejmech.2009.12.038
Yang, X.-G., Chen, D., Wang, M., Xue, Y., Chen, Y.-Z. (2009). Prediction of antibacterial compounds by machine learning approaches. Journal of Computational Chemistry, 30 (8), 1202–1211. doi: 10.1002/jcc.21148
Boiani, M., Cerecetto, H., González, M., Gasteiger, J. (2008). Modeling anti- Trypanosoma cruzi Activity of N -Oxide Containing Heterocycles. Journal of Chemical Information and Modeling, 48 (1), 213–219. doi: 10.1021/ci7002768
Saghaie, L., Shahlaei, M., Madadkar-Sobhani, A., Fassihi, A. (2010). Application of partial least squares and radial basis function neural networks in multivariate imaging analysis-quantitative structure activity relationship: Study of cyclin dependent kinase 4 inhibitors. Journal of Molecular Graphics and Modelling, 29 (4), 518–528. doi: 10.1016/j.jmgm.2010.10.001
Chen, H.-F. (2009). In Silico Log P Prediction for a Large Data Set with Support Vector Machines, Radial Basis Neural Networks and Multiple Linear Regression. Chemical Biology & Drug Design, 74 (2), 142–147. doi: 10.1111/j.1747-0285.2009.00840.x
Lü, W. J., Chen, Y. L., Ma, W. P., Zhang, X. Y., Luan, F., Liu, M. C., Chen, X. G., Hu, Z. D. (2008). QSAR study of neuraminidase inhibitors based on heuristic method and radial basis function network. European Journal of Medicinal Chemistry, 43 (3), 569–576. doi: 10.1016/j.ejmech.2007.04.011
Stempler, S., Levy-Sakin, M., Frydman-Marom, A., Amir, Y., Scherzer-Attali, R., Buzhansky, L., Gazit, E., Senderowitz, H. (2010). Quantitative structure–activity relationship analysis of β-amyloid aggregation inhibitors. Journal of Computer-Aided Molecular Design, 25 (2), 135–144. doi: 10.1007/s10822-010-9405-x
Schattel, V., Hinselmann, G., Jahn, A., Zell, A., Laufer, S. (2011). Modeling and Benchmark Data Set for the Inhibition of c-Jun N-terminal Kinase-3. J. Journal of Chemical Information and Modeling, 51 (3), 670–679. doi: 10.1021/ci100410h
Zimenkovsky, B. S., Devinyak, О. Т., Lesyk, R. B. (2012). Vyvchennia vzaiemozviazku «struktura–protypukhlynna aktyvnist» pokhidnykh 4-tiazolidynoniv metodamy rehresiinoho analizu ta klasyfikatsiinoho modeliuvannia [QSAR study of 4-thiazolidinones as anticancer agents using regression analysis and classification modeling]. Journal of organic and pharmaceutical chemistry, 10/2 (38), 43–49.
Bruce, C. L., Melville, J. L., Pickett, S. D., Hirst, J. D. (2007). Contemporary QSAR Classifiers Compared. Journal of Chemical Information and Modeling, 47 (1), 219–227. doi: 10.1021/ci600332j
Copyright (c) 2015 Олег Теодозійович Девіняк
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.