Title: Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization,
Abstract: 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.