{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:09:01Z","timestamp":1774940941638,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Several kernel-based methods have been recently introduced for the classification of small molecules. Most available kernels on molecules are based on 2D representations obtained from chemical structures, but far less work has focused so far on the definition of effective kernels that can also exploit 3D information.<\/jats:p>\n               <jats:p>Results: We introduce new ideas for building kernels on small molecules that can effectively use and combine 2D and 3D information. We tested these kernels in conjunction with support vector machines for binary classification on the 60 NCI cancer screening datasets as well as on the NCI HIV data set. Our results show that 3D information leveraged by these kernels can consistently improve prediction accuracy in all datasets.<\/jats:p>\n               <jats:p>Availability: An implementation of the small molecule classifier is available from http:\/\/www.dsi.unifi.it\/neural\/src\/3DDK<\/jats:p>\n               <jats:p>Contact: \u00a0costa@dsi.unifi.it<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm298","type":"journal-article","created":{"date-parts":[[2007,6,6]],"date-time":"2007-06-06T01:48:58Z","timestamp":1181094538000},"page":"2038-2045","source":"Crossref","is-referenced-by-count":26,"title":["Classification of small molecules by two- and three-dimensional decomposition kernels"],"prefix":"10.1093","volume":"23","author":[{"given":"Alessio","family":"Ceroni","sequence":"first","affiliation":[{"name":"Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Universit\u00e1 degli Studi di Firenze, Italy"}]},{"given":"Fabrizio","family":"Costa","sequence":"additional","affiliation":[{"name":"Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Universit\u00e1 degli Studi di Firenze, Italy"}]},{"given":"Paolo","family":"Frasconi","sequence":"additional","affiliation":[{"name":"Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Universit\u00e1 degli Studi di Firenze, Italy"}]}],"member":"286","published-online":{"date-parts":[[2007,6,5]]},"reference":[{"key":"2024121118012421100_B1","article-title":"Mining molecular fragments: finding relevant substructures of molecules","author":"Berthold","year":"2002"},{"key":"2024121118012421100_B2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S0097-8485(01)00094-8","article-title":"Drug design by machine learning: support vector machines for pharmaceutical data analysis","volume":"26","author":"Burbidge","year":"2001","journal-title":"Comput. 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