Gutenberg Open Science: Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7432

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DC Field	Value	Language
dc.contributor.author	Gütlein, Martin	-
dc.contributor.author	Kramer, Stefan	-
dc.date.accessioned	2022-07-15T09:37:45Z	-
dc.date.available	2022-07-15T09:37:45Z	-
dc.date.issued	2016
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/7446	-
dc.description.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.	en_GB
dc.description.sponsorship	DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	004 Informatik	de_DE
dc.subject.ddc	004 Data processing	en_GB
dc.title	Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-7432	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Journal of cheminformatics	de
jgu.journal.volume	8	de
jgu.pages.alternative	Art. 60	de
jgu.publisher.year	2016	-
jgu.publisher.name	BioMed Central	de
jgu.publisher.place	London	de
jgu.publisher.uri	http://dx.doi.org/10.1186/s13321-016-0173-z	de
jgu.publisher.issn	1758-2946	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	004	de
opus.date.modified	2018-08-22T10:09:58Z
opus.subject.dfgcode	13-409
opus.organisation.string	FB 08: Physik, Mathematik und Informatik: Institut für Informatik	de_DE
opus.identifier.opusid	55064
opus.institute.number	0805
opus.metadataonly	false
opus.type.contenttype	Keine	de_DE
opus.type.contenttype	None	en_EN
opus.affiliated	Gütlein, Martin
opus.affiliated	Kramer, Stefan
jgu.publisher.doi	10.1186/s13321-016-0173-z	de
jgu.organisation.ror	https://ror.org/023b0x485	-
Appears in collections:	DFG-OA-Publizieren (2012 - 2017)

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