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Crowd computing: using competitive dynamics to develop and refine highly predictive models
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DOI：10.1016/j.drudis.2013.01.002.png)
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A recent application of a crowd computing platform to develop highly predictive in silico models for use in the drug discovery process is described. The platform, Kaggle (TM), exploits a competitive dynamic that results in model optimization as the competition unfolds. Here, this dynamic is described in detail and compared with more-conventional modeling strategies. The complete and full structure of the underlying dataset is disclosed and some thoughts as to the broader utility of such 'gamification' approaches to the field of modeling are offered.
Keyword:
DRUG
QSAR
PERFORMANCE
DISCOVERY
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Next-Generation Model-Based Drug Discovery and Development: Quantitative and Systems Pharmacology
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