Exploring differential evolution for inverse QSAR analysis

F1000Research

Volumn 6, Issue , 2017, Pages

  Bajorath, Jürgen ^b   Miyao, Tomoyuki ^a,b   Funatsu, Kimito ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

Active compounds; Chemical space; Differential evolution; Inverse QSAR; Support vector regression; Virtual screening

Indexed keywords

BLOOD CLOTTING FACTOR 10; BLOOD CLOTTING FACTOR 10 INHIBITOR; CARBONATE DEHYDRATASE II; CARBONIC ANHYDRASE I INHIBITOR; CARBONIC ANHYDRASE II INHIBITOR; CARBONIC ANHYDRASE XII INHIBITOR; NORADRENALIN TRANSPORTER; NOREPINEPHRINE TRANSPORTER LIGAND; OREXIN 2 RECEPTOR; OREXIN RECEPTOR 2 LIGAND; SEROTONIN 1A RECEPTOR; SEROTONIN 1A RECEPTOR LIGAND; SEROTONIN 2A RECEPTOR; SEROTONIN 2A RECEPTOR LIGAND; THROMBIN INHIBITOR; UNCLASSIFIED DRUG;

ARTICLE; COMPUTER SIMULATION; CONTROLLED STUDY; DATA ANALYSIS; EVOLUTIONARY ALGORITHM; INTERMETHOD COMPARISON; MOLECULAR MODEL; PREDICTIVE VALUE; PROCESS OPTIMIZATION; PROTEIN ENGINEERING; PROTEIN STRUCTURE; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 85028989663     PISSN: 20461402     EISSN: 1759796X     Source Type: Journal    
DOI: 10.12688/f1000research.12228.1     Document Type: Article

References (29)

1. Kier LB Hall LH Frazer JW: Design of Molecules from Quantitative Structure-Activity Relationship Models. 1. Information Transfer between Path and Vertex Degree Counts. J Chem Inf Comput Sci. 1993;33(1):143-147. 10.1021/ci00011a021
2. Hall LH Kier LB Frazer JW: Design of Molecules from Quantitative Structure-Activity Relationship Models. 2. Derivation and Proof of Information Transfer Relating Equations. J Chem Inf Comput Sci. 1993;33(1):148-152. 10.1021/ci00011a022
3. Skvortsova MI Baskin II Slovokhotova OL: Inverse Problem in QSAR/QSPR Studies for the Case of Topological Indexes Characterizing Molecular Shape (Kier Indices). J Chem Inf Comput Sci. 1993;33(4):630-634. 10.1021/ci00014a017
4. Skvortsova MI Fedyaev KS Palyulin VA: Inverse Structure-Property Relationship Problem for the Case of a Correlation Equation Containing the Hosoya Index. Dokl Chem. 2001;379(1-3):191-195. 10.1023/A:1019217526008
5. Schneider G Baringhaus KH: De Novo Design: From Models to Molecules. In: De novo Molecular Design. Wiley-VCH Verlag GmbH & Co. KGaA Weinheim Germany;2013;1-55. 10.1002/9783527677016.ch1
6. Speck-Planche A Cordeiro MN: Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins. Mol Divers. 2017;1-13. 28194627 10.1007/s11030-017-9731-1
7. Speck-Planche A Dias Soeiro Cordeiro MN: Speeding up Early Drug Discovery in Antiviral Research: A Fragment-Based in Silico Approach for the Design of Virtual Anti-Hepatitis C Leads. ACS Comb Sci. 2017. 28437091 10.1021/acscombsci.7b00039
8. Faulon JL Visco DP Jr Pophale RS: The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J Chem Inf Comput Sci. 2003;43(3):707-720. 12767129 10.1021/ci020345w
9. Faulon JL Churchwell CJ Visco DP Jr: The signature molecular descriptor. 2. Enumerating molecules from their extended valence sequences. J Chem Inf Comput Sci. 2003;43(3):721-734. 12767130 10.1021/ci020346o
10. Churchwell CJ Rintoul MD Martin S: The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides. J Mol Graph Model. 2004;22(4):263-273. 15177078 10.1016/j.jmgm.2003.10.002
11. Weis DC Faulon JL LeBorne RC: The Signature Molecular Descriptor. 5. The Design of Hydrofluoroether Foam Blowing Agents Using Inverse-QSAR. Ind Eng Chem Res. 2005;44(23):8883-8891. 10.1021/ie050330y
12. Wong WW Burkowski FJ: A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem. J Cheminform. 2009;1:4. 20142987 10.1186/1758-2946-1-4 2816860
13. Miyao T Kaneko H Funatsu K: Ring-System-Based Exhaustive Structure Generation for Inverse-QSPR/QSAR. Mol Inform. 2014;33(11-12):764-778. 27485423 10.1002/minf.201400072
14. Miyao T Kaneko H Funatsu K: Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x). J Chem Inf Model. 2016;56(2):286-299. 26818135 10.1021/acs.jcim.5b00628
15. Miyao T Kaneko H Funatsu K: Ring system-based chemical graph generation for de novo molecular design. J Comput Aided Mol Des. 2016;30(5):425-446. 27299746 10.1007/s10822-016-9916-1
16. Storn R Price K: Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J Global Optim. 1997;11(4):341-359. 10.1023/A:1008202821328
17. Onwubolu G Davendra D: Scheduling Flow Shops Using Differential Evolution Algorithm. Eur J Oper Res. 2006;171(2):674-692. 10.1016/j.ejor.2004.08.043
18. Takahama T Sakai S: Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites. In: 2006 IEEE International Conference on Evolutionary Computation. IEEE;2006;1-8. 10.1109/CEC.2006.1688283
19. Smola AJ Schölkopf B: A Tutorial on Support Vector Regression. Stat Comput. 2004;14(3):199-222. 10.1023/B:STCO.0000035301.49549.88
20. Schölkopf B Platt JC Shawe-Taylor J: Estimating the Support of a High-Dimensional Distribution. Neural Comput. 2001;13(7):1443-1471. 11440593 10.1162/089976601750264965
21. Tang Y Guo W Gao J: Efficient Model Selection for Support Vector Machine with Gaussian Kernel Function. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining; IEEE,2009;40-45. 10.1109/CIDM.2009.4938627
22. Bento AP Gaulton A Hersey A: The ChEMBL bioactivity database: an update. Nucleic Acids Res. 2014;42(Database issue):D1083-D1090. 24214965 10.1093/nar/gkt1031 3965067
23. Baell JB Holloway GA: New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem. 2010;53(7):2719-2740. 20131845 10.1021/jm901137j
24. Hall LH Kier LB: The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure-Property Modeling. John Wiley & Sons, Inc.;2007; 2:367-422. 10.1002/9780470125793.ch9
25. Gasteiger J Marsili M: Iterative Partial Equalization of Orbital Electronegativity - a Rapid Access to Atomic Charges. Tetrahedron. 1980;36(22):3219-3228. 10.1016/0040-4020(80)80168-2
26. Pedregosa F Varoquaux G Gramfort A: Scikit-Learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825-2830. Reference Source
27. Dong X Liu S Tao T: A Comparative Study of Differential Evolution and Genetic Algorithms for Optimizing the Design of Water Distribution Systems. J Zhejiang Univ Sci A. 2012;13(9):674-686. 10.1631/jzus.A1200072
28. Tušar T Filipič B: Differential Evolution versus Genetic Algorithms in Multiobjective Optimization. In: Evolutionary Multi-Criterion Optimization. Springer Berlin Heidelberg: Berlin, Heidelberg;2007;257-271. 10.1007/978-3-540-70928-2_22
29. Iwan M Akmeliawati R Faisal T: Performance Comparison of Differential Evolution and Particle Swarm Optimization in Constrained Optimization. Procedia Eng. 2012;41:1323-1328. 10.1016/j.proeng.2012.07.317