Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x)

Journal of Chemical Information and Modeling

Volumn 56, Issue 2, 2016, Pages 286-299

  Miyao, Tomoyuki ^a   Kaneko, Hiromasa ^a   Funatsu, Kimito ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ANALYSIS; LINEAR REGRESSION; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS;

AQUEOUS SOLUBILITY; INVERSE ANALYSIS; MIXTURE OF GAUSSIANS; MULTIPLE LINEAR REGRESSIONS; POSTERIOR DISTRIBUTIONS; PRIOR DISTRIBUTION; QUANTITATIVE STRUCTURE - PROPERTY RELATIONSHIPS; STRUCTURE GENERATION;

INVERSE PROBLEMS;

CHEMICAL MODEL; CHEMICAL STRUCTURE; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

MODELS, CHEMICAL; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84961128863     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.5b00628     Document Type: Article

References (38)

1. Skvortsova, M. I.; Baskin, I. I.; Slovokhotova, O. L.; Palyulin, V. a; Zefirov, N. S. Inverse Problem in QSAR/QSPR Studies for the Case of Topological Indexes Characterizing Molecular Shape (Kier Indices) J. Chem. Inf. Model. 1993, 33, 630-634 10.1021/ci00014a017
2. Churchwell, C. J.; Rintoul, M. D.; Martin, S.; Visco, D. P.; Kotu, A.; Larson, R. S.; Sillerud, L. O.; Brown, D. C.; Faulon, J.-L. The Signature Molecular Descriptor. 3. Inverse-Quantitative Structure-Activity Relationship of ICAM-1 Inhibitory Peptides J. Mol. Graphics Modell. 2004, 22, 263-273 10.1016/j.jmgm.2003.10.002
3. Faulon, J.-L.; Churchwell, C. J.; Visco, D. P. The Signature Molecular Descriptor. 2. Enumerating Molecules from Their Extended Valence Sequences J. Chem. Inf. Model. 2003, 43, 721-734 10.1021/ci020346o
4. Kier, L. B.; Hall, L. H.; Frazer, J. W. Design of Molecules from Quantitative Structure-Activity Relationship Models. 1. Information Transfer between Path and Vertex Degree Counts J. Chem. Inf. Model. 1993, 33, 143-147 10.1021/ci00011a021
5. Nicolaou, C. A.; Apostolakis, J.; Pattichis, C. S. De Novo Drug Design Using Multiobjective Evolutionary Graphs J. Chem. Inf. Model. 2009, 49, 295-307 10.1021/ci800308h
6. Brown, N.; McKay, B.; Gasteiger, J. A Novel Workflow for the Inverse QSPR Problem Using Multiobjective Optimization J. Comput.-Aided Mol. Des. 2006, 20, 333-341 10.1007/s10822-006-9063-1
7. Kawashita, N.; Yamasaki, H.; Miyao, T.; Kawai, K.; Sakae, Y.; Ishikawa, T.; Mori, K.; Nakamura, S.; Kaneko, H. A Mini-Review on Chemoinformatics Approaches for Drug Discovery Journal of Computer Aided Chemistry 2015, 16, 15-29 10.2751/jcac.16.15
8. Virshup, A. M.; Contreras-García, J.; Wipf, P.; Yang, W.; Beratan, D. N. Stochastic Voyages into Uncharted Chemical Space Produce a Representative Library of All Possible Drug-like Compounds J. Am. Chem. Soc. 2013, 135, 7296-7303 10.1021/ja401184g
9. Miyao, T.; Kaneko, H.; Funatsu, K. Ring-System-Based Exhaustive Structure Generation for Inverse-QSPR/QSAR Mol. Inf. 2014, 33, 764-778 10.1002/minf.201400072
10. Miyao, T.; Arakawa, M.; Funatsu, K. Exhaustive Structure Generation for Inverse-QSPR/QSAR Mol. Inf. 2010, 29, 111-125 10.1002/minf.200900038
11. Jaworska, J.; Nikolova-Jeliazkova, N.; Aldenberg, T. QSAR Applicabilty Domain Estimation by Projection of the Training Set Descriptor Space: A Review Altern. Lab. Anim. 2005, 33, 445-459
12. Fechner, N.; Jahn, A.; Hinselmann, G.; Zell, A. Estimation of the Applicability Domain of Kernel-Based Machine Learning Models for Virtual Screening J. Cheminf. 2010, 2, 2 10.1186/1758-2946-2-2
13. Cohn, D.; Ghahramani, Z.; Jordan, M. Active Learning with Statistical Models Journal of Artificial Intelligence Research 1996, 4, 129-145
14. Eklund, N.; Iyer, N. Anomaly Detection Using Data Clustering and Neural Networks. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence); IEEE, 2008; pp 3627-3633.
15. White, D.; Wilson, R. C. Generative Models for Chemical Structures J. Chem. Inf. Model. 2010, 50, 1257-1274 10.1021/ci9004089
16. Dempster, A.; Laird, N.; Rubin, D. Maximum Likelihood from Incomplete Data via the EM Algorithm Journal of the Royal Statistical Society. Series B 1977, 39, 1-38
17. Burnham, K. P.; Anderson, D. R. Multimodel Inference: Understanding AIC and BIC in Model Selection Sociological Methods Research 2004, 33, 261-304 10.1177/0049124104268644
18. Viele, K.; Tong, B. Modeling with Mixtures of Linear Regressions Statistics and Computing 2002, 12, 315-330 10.1023/A:1020779827503
19. DeSarbo, W. S.; Cron, W. L. A Maximum Likelihood Methodology for Clusterwise Linear Regression Journal of Classification 1988, 5, 249-282 10.1007/BF01897167
20. Manwani, N.; Sastry, P. S. K-Plane Regression Inf. Sci. (N. Y.) 2015, 292, 39-56 10.1016/j.ins.2014.08.058
21. Späth, H. A Fast Algorithm for Clusterwise Linear Regression Computing 1982, 29, 175-181 10.1007/BF02249940
22. Maggiora, G. M. On Outliers and Activity Cliffs - Why QSAR Often Disappoints J. Chem. Inf. Model. 2006, 46, 1535 10.1021/ci060117s
23. Bishop, C. Pattern Recognition and Machine Learning; Springer-Verlag: New York, 2006.
24. Kireeva, N.; Kuznetsov, S. L.; Bykov, A. A.; Tsivadze, A. Y. Towards in Silico Identification of the Human Ether-a-Go-Go-Related Gene Channel Blockers: Discriminative vs. Generative Classification Models SAR QSAR Environ. Res. 2013, 24, 103-117 10.1080/1062936X.2012.742135
25. Caruana, R.; Niculescu-Mizil, A. An Empirical Comparison of Supervised Learning Algorithms. In Proceedings of the 23rd International Conference on Machine Learning-ICML '06; ACM Press: New York, USA, 2006; pp 161-168.
26. Chen, B.; Sheridan, R. P.; Hornak, V.; Voigt, J. H. Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions J. Chem. Inf. Model. 2012, 52, 792-803 10.1021/ci200615h
27. Castilho, M. S.; Postigo, M. P.; de Paula, C. B. V; Montanari, C. A.; Oliva, G.; Andricopulo, A. D. Two- and Three-Dimensional Quantitative Structure-Activity Relationships for a Series of Purine Nucleoside Phosphorylase Inhibitors Bioorg. Med. Chem. 2006, 14, 516-527 10.1016/j.bmc.2005.08.055
28. GVK database. http://www.gvkbio.com/ (accessed January 5, 2016).
29. Ertl, P.; Rohde, B.; Selzer, P. Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties J. Med. Chem. 2000, 43, 3714-3717 10.1021/jm000942e
30. RDKit: Open-source cheminformatics. http://www.rdkit.org (accessed January 5, 2016).
31. Water solublity (logS) database. http://modem.ucsd.edu/adme/databases/databases-logS.htm (accessed January 5, 2016).
32. Hou, T. J.; Xia, K.; Zhang, W.; Xu, X. J. ADME Evaluation in Drug Discovery. 4. Prediction of Aqueous Solubility Based on Atom Contribution Approach J. Chem. Inf. Model. 2004, 44, 266-275 10.1021/ci034184n
33. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development settings1PII of Original Article: S0169-409X(96)00423-1. The Article Was Originally Published in Advanced Drug Delivery Reviews 23 (1997) 3 Adv. Drug Delivery Rev. 2001, 46, 3-26 10.1016/S0169-409X(00)00129-0
34. Fraley, C.; Raftery, A. E. Model-Based Clustering, Discriminant Analysis, and Density Estimation J. Am. Stat. Assoc. 2002, 97, 611-631 10.1198/016214502760047131
35. Calinon, S.; Guenter, F.; Billard, A. On Learning, Representing, and Generalizing a Task in a Humanoid Robot IEEE Trans. Syst. Man Cybern. Part B 2007, 37, 286-298 10.1109/TSMCB.2006.886952
36. Alves de Lima Ribeiro, F.; Ferreira, M. M. C. QSPR Models of Boiling Point, Octanol-water Partition Coefficient and Retention Time Index of Polycyclic Aromatic Hydrocarbons J. Mol. Struct.: THEOCHEM 2003, 663, 109-126 10.1016/j.theochem.2003.08.107
37. Österberg, T.; Norinder, U. Prediction of Drug Transport Processes Using Simple Parameters and PLS Statistics The Use of ACD/logP and ACD/ChemSketch Descriptors Eur. J. Pharm. Sci. 2001, 12, 327-337 10.1016/S0928-0987(00)00189-5
38. Oprea, T. I.; Gottfries, J. Toward Minimalistic Modeling of Oral Drug Absorption J. Mol. Graphics Modell. 1999, 17, 261-274 10.1016/S1093-3263(99)00034-0