{"data":{"id":"10.5281/zenodo.10372418","type":"dois","attributes":{"doi":"10.5281/zenodo.10372418","prefix":"10.5281","suffix":"zenodo.10372418","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Swanson, Kyle","nameType":"Personal","givenName":"Kyle","familyName":"Swanson","affiliation":["Stanford University","Greenstone Biosciences"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-7385-7844","nameIdentifierScheme":"ORCID"}]},{"name":"Walther, Parker","nameType":"Personal","givenName":"Parker","familyName":"Walther","affiliation":["Carleton College"],"nameIdentifiers":[]},{"name":"Leitz, Jeremy","nameType":"Personal","givenName":"Jeremy","familyName":"Leitz","affiliation":["Greenstone Biosciences"],"nameIdentifiers":[]},{"name":"Mukherjee, Souhrid","nameType":"Personal","givenName":"Souhrid","familyName":"Mukherjee","affiliation":["Greenstone Biosciences"],"nameIdentifiers":[]},{"name":"Wu, Joseph C.","nameType":"Personal","givenName":"Joseph C.","familyName":"Wu","affiliation":["Stanford University","Greenstone Biosciences"],"nameIdentifiers":[]},{"name":"Shivnaraine, Rabindra V.","nameType":"Personal","givenName":"Rabindra V.","familyName":"Shivnaraine","affiliation":["Greenstone Biosciences"],"nameIdentifiers":[]},{"name":"Zou, James","nameType":"Personal","givenName":"James","familyName":"Zou","affiliation":["Stanford University","Greenstone Biosciences"],"nameIdentifiers":[]}],"titles":[{"title":"ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries – Data and Models"}],"publisher":"Zenodo","container":{},"publicationYear":2023,"subjects":[],"contributors":[],"dates":[{"date":"2023-12-13","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.10372419","relatedIdentifierType":"DOI"},{"relationType":"IsPartOf","relatedIdentifier":"1367-4811","resourceTypeGeneral":"Collection","relatedIdentifierType":"ISSN"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v1.0.0","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This repository contains data and models used in the following paper.\n\n \n\nSwanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., \u0026 Zou, J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. In review.\n\n \n\nThe data and models are meant to be used with the ADMET-AI code, which runs the ADMET-AI web server at admet.ai.greenstonebio.com.\n\n \n\nThe data.zip file has the following structure.\n\ndata\n\n  drugbank: Contains files with drugs from the DrugBank that have received regulatory approval. drugbank_approved.csv contains the full set of approved drugs along with ADMET-AI predictions, while the other files contain subsets of these molecules used for testing the speed of ADMET prediction tools.\n\n  tdc_admet_all: Contains the data (.csv files) and RDKit features (.npz files) for all 41 single-task ADMET datasets from the Therapeutics Data Commons (TDC).\n\n  tdc_admet_multitask: Contains the data (.csv files) and RDKit features (.npz files) for the two multi-task datasets (one regression and one classification) constructed by combining the tdc_admet_all datasets.\n\n  tdc_admet_all.csv: A CSV file containing all 41 ADMET datasets from tdc_admet_all. This can be used to easily look up all ADMET properties for a given molecule in the TDC.\n\n  tdc_admet_group: Contains the data (.csv files) and RDKit features (.npz files) for the 22 TDC ADMET Benchmark Group datasets with five splits per dataset.\n\n  tdc_admet_group_raw: Contains the raw data (.csv files) used to construct the five splits per dataset in tdc_admet_group.\n\n \n\nThe models.zip file has the following structure. Note that the ADMET-AI website and Python package use the multi-task Chemprop-RDKit models below.\n\nmodels\n\n  tdc_admet_all: Contains Chemprop and Chemprop-RDKit models trained on all 41 single-task TDC ADMET datasets.\n\n  tdc_admet_all_multitask: Contains Chemprop and Chemprop-RDKit models trained on the two multi-task TDC ADMET datasets (one regression and one classification).\n\n  tdc_admet_group: Contains Chemprop and Chemprop-RDKit models trained on the 22 TDC ADMET Benchmark Group datasets.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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