{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/constant-size-molecular-descriptors-for-use","title":"Constant Size Molecular Descriptors For Use With Machine Learning","arxiv_id":"1701.06649","date":"2017-01-23","proceeding":null,"authors":["Christopher R. Collins","Geoffrey J. Gordon","O. Anatole von Lilienfeld","David J. Yaron"],"abstract":"A set of molecular descriptors whose length is independent of molecular size\nis developed for machine learning models that target thermodynamic and\nelectronic properties of molecules. These features are evaluated by monitoring\nperformance of kernel ridge regression models on well-studied data sets of\nsmall organic molecules. The features include connectivity counts, which\nrequire only the bonding pattern of the molecule, and encoded distances, which\nsummarize distances between both bonded and non-bonded atoms and so require the\nfull molecular geometry. In addition to having constant size, these features\nsummarize information regarding the local environment of atoms and bonds, such\nthat models can take advantage of similarities resulting from the presence of\nsimilar chemical fragments across molecules. Combining these two types of\nfeatures leads to models whose performance is comparable to or better than the\ncurrent state of the art. The features introduced here have the advantage of\nleading to models that may be trained on smaller molecules and then used\nsuccessfully on larger molecules.","url_abs":"http://arxiv.org/abs/1701.06649v1","url_pdf":"http://arxiv.org/pdf/1701.06649v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"constant-size-molecular-descriptors-for-use","repo_url":"https://github.com/jsheng7/ANI1-qm7","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}