title: Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability.
abstract: BACKGROUND
  Even though circular fingerprints have been first introduced more than 50 years ago, they are still widely used for building highly predictive, state-of-the-art (Q)SAR models. Historically, these structural fragments were designed to search large molecular databases. Hence, to derive a compact representation, circular fingerprint fragments are often folded to comparatively short bit-strings. However, folding fingerprints introduces bit collisions, and therefore adds noise to the encoded structural information and removes its interpretability. Both representations, folded as well as unprocessed fingerprints, are often used for (Q)SAR modeling.
  
  
  RESULTS
  We show that it can be preferable to build (Q)SAR models with circular fingerprint fragments that have been filtered by supervised feature selection, instead of applying folded or all fragments. Compared to folded fingerprints, filtered fingerprints significantly increase predictive performance and remain unambiguous and interpretable. Compared to unprocessed fingerprints, filtered fingerprints reduce the computational effort and are a more compact and less redundant feature representation. Depending on the selected learning algorithm filtering yields about equally predictive (Q)SAR models. We demonstrate the suitability of filtered fingerprints for (Q)SAR modeling by presenting our freely available web service Collision-free Filtered Circular Fingerprints that provides rationales for predictions by highlighting important structural features in the query compound (see http://coffer.informatik.uni-mainz.de).
  
  
  CONCLUSIONS
  Circular fingerprints are potent structural features that yield highly predictive models and encode interpretable structural information. However, to not lose interpretability, circular fingerprints should not be folded when building prediction models. Our experiments show that filtering is a suitable option to reduce the high computational effort when working with all fingerprint fragments. Additionally, our experiments suggest that the area under precision recall curve is a more sensible statistic for validating (Q)SAR models for virtual screening than the area under ROC or other measures for early recognition.
authors.affiliation: Chair of Data Mining, Institute of Computer Science, Johannes Gutenberg - Universität Mainz, Staudingerweg 9, 55128 Mainz, Germany.
authors.forename: Martin
authors.identifier: 0000-0003-2939-2469
authors.initials: M
authors.lastname: Gütlein
authors.affiliation: Chair of Data Mining, Institute of Computer Science, Johannes Gutenberg - Universität Mainz, Staudingerweg 9, 55128 Mainz, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Kramer
keywords: (Q)SAR; Feature selection; Fingerprints; Virtual screening