{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T03:03:22Z","timestamp":1789873402800,"version":"4.0.1"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Partnership for the Assessment of Risks from Chemicals"},{"name":"European Union\u2019s Horizon Europe research and innovation program","award":["101057014"],"award-info":[{"award-number":["101057014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Sophisticated approaches for the in silico prediction of toxicity are required to support the risk assessment of chemicals. The number of chemicals on the global chemical market and the speed of chemical innovation stand in massive contrast to the capacity for regularizing chemical use. We recently proved our ready-to-use application deepFPlearn as a suitable approach for this task. Here, we present its extension deepFPlearn+ incorporating (i) a graph neural network to feed our AI with a more sophisticated molecular structure representation and (ii) alternative train-test splitting strategies that involve scaffold structures and the molecular weights of chemicals. We show that the GNNs outperform the previous model substantially and that our models can generalize on unseen data even with a more robust and challenging test set. Therefore, we highly recommend the application of deepFPlearn+ on the chemical inventory to prioritize chemicals for experimental testing or any chemical subset of interest in monitoring studies.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The software is compatible with python 3.6 or higher, and the source code can be found on our GitHub repository: https:\/\/github.com\/yigbt\/deepFPlearn. The data underlying this article are available in Zenodo, and can be accessed with the link below: https:\/\/zenodo.org\/record\/8146252. Detailed installation guides via Docker, Singularity, and Conda are provided within the repository for operability across all operating systems.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad713","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T23:27:49Z","timestamp":1701127669000},"source":"Crossref","is-referenced-by-count":6,"title":["<tt>deepFPlearn<\/tt>\n          +: enhancing toxicity prediction across the chemical universe using graph neural networks"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1778-7353","authenticated-orcid":false,"given":"Kyriakos","family":"Soulios","sequence":"first","affiliation":[{"name":"Department of Computation Biology, Helmholtz Centre for Environmental Research \u2013 UFZ , 04318 Leipzig, Germany"},{"name":"Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig , 04109 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0361-9063","authenticated-orcid":false,"given":"Patrick","family":"Scheibe","sequence":"additional","affiliation":[{"name":"Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , 04103 Leipzig, Saxony, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3763-0797","authenticated-orcid":false,"given":"Matthias","family":"Bernt","sequence":"additional","affiliation":[{"name":"Department of Computation Biology, Helmholtz Centre for Environmental Research \u2013 UFZ , 04318 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4920-7072","authenticated-orcid":false,"given":"J\u00f6rg","family":"Hackerm\u00fcller","sequence":"additional","affiliation":[{"name":"Department of Computation Biology, Helmholtz Centre for Environmental Research \u2013 UFZ , 04318 Leipzig, Germany"},{"name":"Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig , 04109 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1200-6234","authenticated-orcid":false,"given":"Jana","family":"Schor","sequence":"additional","affiliation":[{"name":"Department of Computation Biology, Helmholtz Centre for Environmental Research \u2013 UFZ , 04318 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"2023121610214220600_btad713-B1","doi-asserted-by":"crossref","first-page":"2887","DOI":"10.1021\/jm9602928","article-title":"The properties of known drugs. 1. 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