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Autoren: Gütlein, Martin
Kramer, Stefan
Titel: Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability
Online-Publikationsdatum: 15-Jul-2022
Erscheinungsdatum: 2016
Sprache des Dokuments: Englisch
Zusammenfassung/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 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.
DDC-Sachgruppe: 004 Informatik
004 Data processing
Veröffentlichende Institution: Johannes Gutenberg-Universität Mainz
Organisationseinheit: FB 08 Physik, Mathematik u. Informatik
Veröffentlichungsort: Mainz
Version: Published version
Publikationstyp: Zeitschriftenaufsatz
Nutzungsrechte: CC BY
Informationen zu den Nutzungsrechten:
Zeitschrift: Journal of cheminformatics
Seitenzahl oder Artikelnummer: Art. 60
Verlag: BioMed Central
Verlagsort: London
Erscheinungsdatum: 2016
ISSN: 1758-2946
URL der Originalveröffentlichung:
DOI der Originalveröffentlichung: 10.1186/s13321-016-0173-z
Enthalten in den Sammlungen:DFG-OA-Publizieren (2012 - 2017)

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