Details zur Publikation

Referenztyp Zeitschriften
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Creative Commons Lizenz creative commons licence
Titel (primär) AI for predicting chemical-effect associations at the chemical universe level — deepFPlearn
Autor Schor, J.; Scheibe, P.; Bernt, M.; Busch, W.; Lai, C.; Hackermüller, J.
Journal / Serie Briefings in Bioinformatics
Erscheinungsjahr 2022
Department BIOTOX; BIOINF
Band/Volume 23
Heft 5
Seite von bbac257
Sprache englisch
Topic T9 Healthy Planet
Supplements https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/bib/PAP/10.1093_bib_bbac257/1/supplement_bbac257.pdf?Expires=1662107365&Signature=tKyYvlV1GnDvHk~Qf-3h44WfeFzMQ7NgOOIDK9Ygm9aiLS9SIN2Tri4E5NNd~OMk29n4uT5JeAvigv4TcsfRSe5cEEgXZxjFeZR8D4VY90-qinP2Jb6b606RxDvJA1jbYBVjuMzvoTLOdzr1Tk9iETpBTtIpJMkWcNsGQKirxY1cqo01KsGuAxeJ2FDBBnYD-UyU-8zKYDQOKaBsI1ZMirdHZLH-XwdBh8dRyOhe6XjqnTmeFCAn7iVRvquixiPicNkFpM-I7LEpxIh-HWv6JtnBFe2Qqb3zRo1faeA43CQ-CVjSTy0pP0zw18cghLr0coAqfA4hyXVtXrXcByIk3Q__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA
Keywords Deep learning; toxicology; binary fingerprint; autoencoder; molecular structures
Abstract 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—even if high throughput—are 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—experimentally verified—associations 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.
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=26413
Schor, J., Scheibe, P., Bernt, M., Busch, W., Lai, C., Hackermüller, J. (2022):
AI for predicting chemical-effect associations at the chemical universe level — deepFPlearn
Brief. Bioinform. 23 (5), bbac257