Scaling predictive modeling in drug development with cloud computing

Journal of Chemical Information and Modeling

Volumn 55, Issue 1, 2015, Pages 19-25

  Moghadam, Behrooz Torabi ^a   Alvarsson, Jonathan ^a   Holm, Marcus ^a   Eklund, Martin ^a   Carlsson, Lars ^b   Spjuth, Ola ^a  

^a UPPSALA UNIVERSITY   (Sweden)

^b ASTRAZENECA R AND D   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

CLOUD COMPUTING; CLUSTER COMPUTING; DRUG PRODUCTS; ECONOMICS; MODEL BUILDINGS; OPEN DATA; SUPERCOMPUTERS;

COMPUTATIONAL RESOURCES; DRUG DEVELOPMENT; DRUG DISCOVERY; HIGH PERFORMANCE COMPUTERS; HIGH PERFORMANCE COMPUTING; HIGH-PERFORMANCE COMPUTING CLUSTERS; PREDICTIVE MODELING; PREDICTIVE MODELS;

PREDICTIVE ANALYTICS;

LIGAND;

BIOLOGY; CHEMICAL DATABASE; COMPUTER ANALYSIS; COMPUTER PROGRAM; DRUG DEVELOPMENT; FACTUAL DATABASE; INTERNET; PROCEDURES; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

COMPUTATIONAL BIOLOGY; COMPUTING METHODOLOGIES; DATABASES, CHEMICAL; DATABASES, FACTUAL; DRUG DISCOVERY; INTERNET; LIGANDS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOFTWARE;

EID: 84921662520     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci500580y     Document Type: Article

References (62)

1. Maclean, D.; Kamoun, S. Big data in small places Nat. Biotechnol. 2012, 30, 33-34
2. Schadt, E. E.; Linderman, M. D.; Sorenson, J.; Lee, L.; Nolan, G. P. Computational solutions to large-scale data management and analysis Nat. Rev. Genet. 2010, 11, 647-657
3. Mell, P.; Grance, T. The NIST Definition of Cloud Computing; National Institute of Standards and Technology, Information Technology Laboratory, 2009.
4. Amazon Elastic Compute Cloud (Amazon EC2). http://aws.amazon.com/ec2/ (Accessed Nov. 14, 2014).
5. Microsoft Windows Azure Services Platform. http://www.windowsazure.com/ (Accessed Nov. 14, 2014).
6. Krampis, K.; Booth, T.; Chapman, B.; Tiwari, B.; Bicak, M.; Field, D.; Nelson, K. E. Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community BMC Bioinformatics 2012, 13, 42
7. Stein, L. D. The case for cloud computing in genome informatics Genome Biol. 2010, 11, 207
8. Gathering clouds and a sequencing storm: why cloud computing could broaden community access to next-generation sequencing. Nat. Biotechnol. 2010, 28, 1.
9. Sansom, C. Up in a cloud? Nat. Biotechnol. 2010, 28, 13-15
10. Schatz, M. C.; Langmead, B.; Salzberg, S. L. Cloud computing and the DNA data race Nat. Biotechnol. 2010, 28, 691-693
11. Mohammed, Y.; Mostovenko, E.; Henneman, A. A.; Marissen, R. J.; Deelder, A. M.; Palmblad, M. Cloud parallel processing of tandem mass spectrometry based proteomics data J. Proteome Res. 2012, 11, 5101-5108
12. Jourdren, L.; Bernard, M.; Dillies, M.-A.; Le Crom, S. Eoulsan: a cloud computing-based framework facilitating high throughput sequencing analyses Bioinformatics 2012, 28, 1542-1543
13. Langmead, B.; Hansen, K. D.; Leek, J. T. Cloud-scale RNA-sequencing differential expression analysis with Myrna Genome Biol. 2010, 11, R83
14. Langmead, B.; Schatz, M. C.; Lin, J.; Pop, M.; Salzberg, S. L. Searching for SNPs with cloud computing Genome Biol. 2009, 10, R134
15. Via, M.; Gignoux, C.; Burchard, E. G. The 1000 Genomes Project: new opportunities for research and social challenges Genome Med. 2010, 2, 3
16. Dudley, J. T.; Butte, A. J. In silico research in the era of cloud computing Nat. Biotechnol. 2010, 28, 1181-1185
17. Wagener, J.; Spjuth, O.; Willighagen, E. L.; Wikberg, J. E. S. XMPP for cloud computing in bioinformatics supporting discovery and invocation of asynchronous web services BMC Bioinformatics 2009, 10, 279
18. Afgan, E.; Baker, D.; Coraor, N.; Goto, H.; Paul, I. M.; Makova, K. D.; Nekrutenko, A.; Taylor, J. Harnessing cloud computing with Galaxy Cloud Nat. Biotechnol. 2011, 29, 972-974
19. Abouelhoda, M.; Issa, S. A.; Ghanem, M. Tavaxy: Integrating Taverna and Galaxy workflows with cloud computing support BMC Bioinformatics 2012, 13, 77
20. Hunter, A. A.; Macgregor, A. B.; Szabo, T. O.; Wellington, C. A.; Bellgard, M. I. Yabi: An online research environment for grid, high performance and cloud computing Source Code Biol. Med. 2012, 7, 1
21. Pitera, J. W. Current developments in and importance of high-performance computing in drug discovery Curr. Opin. Drug Discovery Devel. 2009, 12, 388-396
22. Valerio, L. G., Jr.; Choudhuri, S. Chemoinformatics and chemical genomics: potential utility of in silico methods J. Appl. Toxicol. 2012, 32, 880-889
23. Sun, H.; Xia, M.; Austin, C. P.; Huang, R. Paradigm shift in toxicity testing and modeling AAPS journal 2012, 14, 473-480
24. Amazon Web Services. http://aws.amazon.com (Accessed Nov. 14, 2014).
25. Advancing Drug Discovery with HPC Cloud. http://www.hpcwire.com/2014/07/10/advancing-drug-discovery-hpc-cloud/ (Accessed Nov. 14, 2014).
26. D'Agostino, D.; Clematis, A.; Quarati, A.; Cesini, D.; Chiappori, F.; Milanesi, L.; Merelli, I. Cloud infrastructures for in silico drug discovery: economic and practical aspects Biomed Res. Int. 2013, 138012
27. Williams, A. J.; Harland, L.; Groth, P.; Pettifer, S.; Chichester, C.; Willighagen, E. L.; Evelo, C. T.; Blomberg, N.; Ecker, G.; Goble, C.; Mons, B. Open PHACTS: semantic interoperability for drug discovery Drug Discov Today 2012, 17, 1188-1198
28. Mortelmans, K.; Zeiger, E. The Ames Salmonella/microsome mutagenicity assay Mutat. Res. 2000, 455, 29-60
29. Kazius, J.; McGuire, R.; Bursi, R. Derivation and validation of toxicophores for mutagenicity prediction J. Med. Chem. 2005, 48, 312-320
30. C, H. A Quantitative Approach to Biochemical Structure-Activity Relationships Acc. Chem. Res. 1969, 2, 232-239
31. Helma, C. Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity Mol. Divers. 2006, 10, 147-158
32. Helguera, A. M.; Gonzalez, M. P.; Dias Soeiro Cordeiro, M. N.; Cabrera Perez, M. A. Quantitative Structure-Carcinogenicity Relationship for Detecting Structural Alerts in Nitroso Compounds: Species, Rat; Sex, Female; Route of Administration, Gavage Chem. Res. Toxicol. 2008, 21, 633-642
33. Spycher, S.; Smejtek, P.; Netzeva, T. I.; Escher, B. I. Toward a Class-Independent Quantitative Structure-Activity Relationship Model for Uncouplers of Oxidative Phosphorylation Chem. Res. Toxicol. 2008, 21, 911-927
34. Guha, R.; Schürer, S. Utilizing High Throughput Screening Data for Predictive Toxicology Models: Protocols and Application to MLSCN Assays J. Comp. Aid. Mol. Des. 2008, 22, 367-384
35. Johnson, S.; Chen, X.; Murphy, D.; Gudmundsson, O. A Computational Model for the Prediction of Aqueous Solubility That Includes Crystal Packing, Intrinsic Solubility, and Ionization Effects Mol. Pharmaceutics 2007, 4, 513-523
36. Yan, A.; Gasteiger, J. Prediction of Aqueous Solubility of Organic Compounds Based on 3D Structure Representation J. Chem. Inf. Comput. Sci. 2003, 43, 429-434
37. Munteanu, C. R.; Fernández-Blanco, E.; Seoane, J. A.; Izquierdo-Novo, P.; Rodrguez-Fernández, J. A.; Prieto-González, J. M.; Rabuñal, J. R.; Pazos, A. Drug discovery and design for complex diseases through QSAR computational methods Curr. Pharm. Des. 2010, 16, 2640-2655
38. Gedeck, P.; Lewis, R. A. Exploiting QSAR models in lead optimization Curr. Opin. Drug Discovery Devel. 2008, 11, 569-575
39. Faulon, J.-L.; Churchwell, C. J.; Visco, D. P. J. The signature molecular descriptor. 2. Enumerating molecules from their extended valence sequences J. Chem. Inf. Comput. Sci. 2003, 43, 721-734
40. Faulon, J.-L.; Visco, D. P. J.; Pophale, R. S. The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies J. Chem. Inf. Comput. Sci. 2003, 43, 707-720
41. Spjuth, O.; Eklund, M.; Ahlberg Helgee, E.; Boyer, S.; Carlsson, L. Integrated decision support for assessing chemical liabilities J. Chem. Inf. Model. 2011, 51, 1840-1847
42. Norinder, U.; Ek, M. E. QSAR investigation of NaV1.7 active compounds using the SVM/Signature approach and the Bioclipse Modeling platform Bioorg. Med. Chem. Lett. 2013, 23, 261-263
43. Alvarsson, J.; Eklund, M.; Engkvist, O.; Spjuth, O.; Carlsson, L.; Wikberg, J. E. S.; Noeske, T. Ligand-based target prediction with signature fingerprints J. Chem. Inf. Model. 2014, 54, 2647-2653
44. Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 1 st ed.; Cambridge University Press, 2000.
45. Steinbeck, C.; Han, Y.; Kuhn, S.; Horlacher, O.; Luttmann, E.; Willighagen, E. The Chemistry Development Kit (CDK): an open-source Java library for Chemo- and Bioinformatics J. Chem. Inf. Comput. Sci. 2003, 43, 493-500
46. Steinbeck, C.; Hoppe, C.; Kuhn, S.; Floris, M.; Guha, R.; Willighagen, E. L. Recent developments of the chemistry development kit (CDK)-an open-source java library for chemo- and bioinformatics Curr. Pharm. Des. 2006, 12, 2111-2120
47. Chang, C.-C.; Lin, C.-J. LIBSVM: A library for support vector machines ACM Trans. Intell. Syst. Technol. 2011, 2, 27
48. piSVM. http://pisvm.sourceforge.net/ (Accessed Nov. 14, 2014).
49. Fusaro, V. A.; Patil, P.; Gafni, E.; Wall, D. P.; Tonellato, P. J. Biomedical cloud computing with Amazon Web Services PLoS Comput. Biol. 2011, 7 e1002147
50. Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX). http://www.uppmax.uu.se (Accessed Nov. 14, 2014).
51. Lampa, S.; Dahlö, M.; Olason, P. I.; Hagberg, J.; Spjuth, O. Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data Gigascience 2013, 2, 9
52. AWS Toolkit for Eclipse. https://aws.amazon.com/eclipse/ (Accessed Nov. 14, 2014).
53. Github repository for AWS interaction. https://github.com/behroozt/Java-AWS-interaction (Accessed Nov. 14, 2014).
54. Brugger, D. Parallel Support Vector Machines; WSI; Graphisch-Interaktive Systeme, Wilhelm-Schickard-Inst. für Informatik, 2006.
55. Menon, A. K. Large-Scale Support Vector Machines: Algorithms and Theory. M.Sc. thesis, University of California, San Diego, 2009.
56. You, Y.; Song, S.; Fu, H.; Marquez, A.; Dehnavi, M.; Barker, K.; Cameron, K.; Randles, A.; Yang, G. MIC-SVM: Designing a Highly Efficient Support Vector Machine for Advanced Modern Multi-core and Many-Core Architectures. 2014 IEEE 28th International Parallel and Distributed Processing Symposium, Phoenix, AZ, May 19-23, 2014; pp 809-818.
57. Amazon EC2 Spot Instances. http://aws.amazon.com/ec2/purchasing-options/spot-instances/ (Accessed Nov. 14, 2014).
58. Chopra, M.; Mungi, J.; Chopra, K. A Survey on Use of Cloud Computing in various Fields Int. J. of Sci. Eng. Technol. Res. 2013, 2, 2
59. Hawkins, D. M. The problem of overfitting J. Chem. Inf. Comput. Sci. 2004, 44, 1-12
60. Reunanen, J. Overfitting in Making Comparisons Between Variable Selection Methods J. Mach. Learn. Res. 2003, 3, 1371-1382
61. Eklund, M.; Spjuth, O.; Wikberg, J. E. The C1C2: a framework for simultaneous model selection and assessment BMC Bioinformatics 2008, 9, 360
62. Alvarsson, J.; Eklund, M.; Andersson, C.; Carlsson, L.; Spjuth, O.; Wikberg, J. E. S. Benchmarking study of parameter variation when using signature fingerprints together with support vector machines J. Chem. Inf. Model. 2014, 54, 3211-3217