{"id":18075623,"url":"https://github.com/ffstghc/caco2ml","last_synced_at":"2025-10-18T05:49:05.793Z","repository":{"id":259588094,"uuid":"873867685","full_name":"ffstghc/caco2ml","owner":"ffstghc","description":"Main code chunks used for models in the publication \"Exploring the Potential of Adaptive, Local Machine Learning (ML) in Comparison ton the Prediction Performance of Global Models: A Case Study from Bayer's Caco-2 Permeability Database\"","archived":false,"fork":false,"pushed_at":"2024-11-21T10:11:09.000Z","size":28,"stargazers_count":2,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-27T19:50:12.117Z","etag":null,"topics":["caco-2","local-models","machine-learning","pharmacokinetics","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ffstghc.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-10-16T21:21:26.000Z","updated_at":"2025-03-26T12:22:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"21208158-89e0-415d-81f2-83f2b448e464","html_url":"https://github.com/ffstghc/caco2ml","commit_stats":null,"previous_names":["ffstghc/caco2ml"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ffstghc/caco2ml","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ffstghc%2Fcaco2ml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ffstghc%2Fcaco2ml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ffstghc%2Fcaco2ml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ffstghc%2Fcaco2ml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ffstghc","download_url":"https://codeload.github.com/ffstghc/caco2ml/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ffstghc%2Fcaco2ml/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":272303500,"owners_count":24910391,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-27T02:00:09.397Z","response_time":76,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["caco-2","local-models","machine-learning","pharmacokinetics","scikit-learn"],"created_at":"2024-10-31T11:06:40.406Z","updated_at":"2025-10-18T05:49:05.669Z","avatar_url":"https://github.com/ffstghc.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# \"Exploring the Potential of Adaptive, Local Machine Learning (ML) in Comparison to the Prediction Performance of Global Models: A Case Study from Bayer's Caco-2 Permeability Database\"\n## _American Chemical Society (ACS): Journal of Chemical Information and Modeling (JCIM)_\n### **_Frank Filip Steinbauer, Thorsten Lehr, Andreas Reichel_**\n### http://pubs.acs.org/doi/abs/10.1021/acs.jcim.4c01083\n\n\nRepository for archiving the main code chunks used for the local and global machine learning models in the publication **_\"Exploring the Potential of Adaptive, Local Machine Learning (ML) in Comparison ton the Prediction Performance of Global Models: A Case Study from Bayer's Caco-2 Permeability Database\"_** published in 2024 in **_ACS Journal of Chemical Information and Modeling (JCIM)_** as 1st publication of my doctoral studies at Bayer.\n\nThe five different included files contain the main code chunks for:\n1. Data preparation (SMILES/molecule object standardization; PaDEL descriptor calculation)\n2. Global models (including other descriptor calculations and recursive feature elimination with cross-validation as well as external TDC benchmarking\u003csup\u003e[1]\u003c/sup\u003e)\n3. Local model (training data selection via fixed tanimoto similarity criteria)\n4. Local model (training data selection via fixed amounts of most similar structuress)\n5. Local model (training data selection via kNN\u003csup\u003e[2]\u003c/sup\u003e as control/proof of superiority of the chosen tanimoto similarity approach)\n\nIf you have further questions or need additional parts of the utilized code for your own studies, feel free to contact Filip.Steinbauer@bayer.com.\n\n[1]: https://tdcommons.ai/single_pred_tasks/adme#caco-2-cell-effective-permeability-wang-et-al\n[2]: https://scikit-learn.org/dev/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fffstghc%2Fcaco2ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fffstghc%2Fcaco2ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fffstghc%2Fcaco2ml/lists"}