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Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization.

08:00 EDT 26th April 2018 | BioPortfolio

Summary of "Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization."

Undetected overfitting can occur when there are significant redundancies between training and validation data. We describe AVE, a new measure of training-validation redundancy for ligand-based classification problems that accounts for the similarity amongst inactive molecules as well as active. We investigated seven widely-used benchmarks for virtual screening and classification, and show that the amount of AVE bias strongly correlates with the performance of ligand-based predictive methods irrespective of the predicted property, chemical fingerprint, similarity measure, or previously-applied unbiasing techniques. Therefore, it may be that the previously-reported performance of most ligand-based methods can be explained by overfitting to benchmarks rather than good prospective accuracy.

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This article was published in the following journal.

Name: Journal of chemical information and modeling
ISSN: 1549-960X
Pages:

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