{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:36Z","timestamp":1772138016754,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T00:00:00Z","timestamp":1657929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Helmholtz Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods\u2014even if high throughput\u2014are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene\/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful\u2014experimentally verified\u2014associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https:\/\/github.com\/yigbt\/deepFPlearn.<\/jats:p>","DOI":"10.1093\/bib\/bbac257","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:12:45Z","timestamp":1658142765000},"source":"Crossref","is-referenced-by-count":6,"title":["AI for predicting chemical-effect associations at the chemical universe level\u2014\n                    <tt>deepFPlearn<\/tt>"],"prefix":"10.1093","volume":"23","author":[{"given":"Jana","family":"Schor","sequence":"first","affiliation":[{"name":"Department Computational Biology, Helmholtz Centre for environmental research - UFZ , Permoserstr. 15, 04318 Leipzig, Saxony , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Scheibe","sequence":"additional","affiliation":[{"name":"Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences , Stephanstra\u03b2e 1a, 04103 Leipzig, Saxony , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"Bernt","sequence":"additional","affiliation":[{"name":"Department Computational Biology, Helmholtz Centre for environmental research - UFZ , Permoserstr. 15, 04318 Leipzig, Saxony , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wibke","family":"Busch","sequence":"additional","affiliation":[{"name":"Department of Bioanalytical Ecotoxicology, Helmholtz Centre for environmental research - UFZ , Permoserstr. 15, 04318 Leipzig, Saxony , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih","family":"Lai","sequence":"additional","affiliation":[{"name":"Graduate Program in Software & School of Engineering, University of St. Thomas , 2115 Summit Ave, St. Paul, MN 55105, Minnesota , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg","family":"Hackerm\u00fcller","sequence":"additional","affiliation":[{"name":"Department Computational Biology, Helmholtz Centre for environmental research - UFZ , Permoserstr. 15, 04318 Leipzig, Saxony , Germany"},{"name":"Department of Computer Science, Leipzig University , Augustuspl. 10, 04109 Leipzig, Saxony , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,7,17]]},"reference":[{"key":"2022092013210812200_ref1","author":"Classification on imbalanced data","year":"2022"},{"key":"2022092013210812200_ref2","first-page":"265","volume-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016","author":"Abadi","year":"2016"},{"issue":"August","key":"2022092013210812200_ref3","first-page":"1072","article-title":"The Challenge of Micropollutants in Aquatic Systems","volume":"313","author":"Anderson","year":"2006","journal-title":"Science"},{"key":"2022092013210812200_ref4","first-page":"9","article-title":"An open source chemical structure curation pipeline using rdkit","volume":"12","author":"Bento","year":"2020","journal-title":"J Chem"},{"key":"2022092013210812200_ref5","volume-title":"Experiment tracking with weights and biases","author":"Biewald","year":"2020"},{"issue":"11-12","key":"2022092013210812200_ref6","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1177\/0748233719893198","article-title":"Inventory and evaluation of publicly available sources of information on hazards and risks of industrial chemicals","volume":"35","author":"Bond","year":"2019","journal-title":"Toxicol Ind Health"},{"issue":"8","key":"2022092013210812200_ref7","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1002\/etc.3460","article-title":"Micropollutants in European rivers: A mode of action survey to support the development of effect-based tools for water monitoring","volume":"35","author":"Busch","year":"2016","journal-title":"Environ Toxicol Chem"},{"key":"2022092013210812200_ref8","author":"Cas. 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