imago_console -dir /path/of/input/image_dir/ Img2Mol uses an encoder-decoder architecture. The original version of Img2Mol relies on an HTTP request of the encoded image to a server hosted by Bayer where the decoder is running. As the web server is only meant to be used for demonstration purposes, we contributed to Img2Mol to create a version that runs the decoder locally instead of sending HTTP requests to server . This standalone version has been used to process all available benchmark datasets by running. The content of the script img2mol_batch_run.py is given in the supporting information (see Code Resource 1 in the supplementary information). python img2mol_batch_run.py /input/path/ output/path png As the original version of SwinOCSR did not include an inference script, we contributed to the open-source project to facilitate the usage of the model with the best performance according to the authors (focal loss model) . After cloning the repository and preparing the environment according to the instructions given there, it was used by running the following command in the directory that contains the scripts related to the above-mentioned model in the repository (SwinOCSR/model/Swin-transformer-focalloss/): python run_ocsr_on_images.py --data-path /path/to/directory/with/images/ DECIMER Image Classifier Generation of chemical structure depictions Chemical structures were depicted as PNG images using the open-source toolkit RanDepict . Five different chemical structure depictions were generated for each entry in the ChEMBL30 70 database (2,157,379 compounds) and the COCONUT database (407,270 natural products). Once the chemical structure depictions were generated, the number of images without chemical structures was determined (6,814,929). In the next step, the same number of images with chemical structures was randomly selected. Following the selection of images with chemical structures, the dataset was randomly divided into training, validation, and test sets based on the 80:16:4 ratio. The result was a training data set containing 5,452,557 structure depictions, a validation data set containing 1,089,899 depictions, and a test data set containing 272,473 depictions.