000K utf8 1100 2021$c2021-08-17 1500 eng 2050 urn:nbn:de:gbv:27-dbt-20230323-203822-003 2051 10.1186/s13321-021-00538-8 3000 Rajan, Kohulan 3010 Steinbeck, Christoph 3010 Zielesny, Achim 4000 DECIMER 1.0: deep learning for chemical image recognition using transformers [Rajan, Kohulan] 4060 16 Seiten 4209 The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, Optical Chemical Structure Recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50–100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information. 4950 https://doi.org/10.1186/s13321-021-00538-8$xR$3Volltext$534 4950 https://nbn-resolving.org/urn:nbn:de:gbv:27-dbt-20230323-203822-003$xR$3Volltext$534 4961 https://www.db-thueringen.de/receive/dbt_mods_00056071 5051 540 5550 Chemical data extraction 5550 Deep learning 5550 Neural networks 5550 Optical chemical structure recognition