{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:27:16Z","timestamp":1781105236909,"version":"3.54.1"},"reference-count":16,"publisher":"IGI Global Scientific Publishing","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,1,1]]},"abstract":"<p>The \u201cMolecular Similarity Principle\u201d states that structurally similar molecules tend to have similar properties\u2014physicochemical and biological. The question then is how to define \u201cstructural similarity\u201d algorithmically and confirm its usefulness. Within this framework, research by similarity is registered, which is a practical approach to identify molecule candidates (to become drugs or medicines) from databases or virtual chemical libraries by comparing the compounds two by two. Many statistical models and learning tools have been developed to correlate the molecules\u2019 structure with their chemical, physical or biological properties. The role of data mining in chemistry is to evaluate \u201chidden\u201d information in a set of chemical data. Each molecule is represented by a vector of great dimension (using molecular descriptors), the applying a learning algorithm on these vectors. In this paper, the authors study the molecular similarity using a hybrid approach based on Self-Organizing Neural Networks and Knn Method.<\/p>","DOI":"10.4018\/ijcce.2011010106","type":"journal-article","created":{"date-parts":[[2011,2,15]],"date-time":"2011-02-15T15:45:12Z","timestamp":1297784712000},"page":"75-95","source":"Crossref","is-referenced-by-count":1,"title":["A Hybrid Approach Based on Self-Organizing Neural Networks and the K-Nearest Neighbors Method to Study Molecular Similarity"],"prefix":"10.4018","volume":"1","author":[{"given":"Abdelmalek","family":"Amine","sequence":"first","affiliation":[{"name":"Tahar Moulay University, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3391-6280","authenticated-orcid":true,"given":"Zakaria","family":"Elberrichi","sequence":"additional","affiliation":[{"name":"Djillali Liabes University, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michel","family":"Simonet","sequence":"additional","affiliation":[{"name":"Fourier University, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Rahmouni","sequence":"additional","affiliation":[{"name":"Tahar Moulay University, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijcce.2011010106-0","doi-asserted-by":"publisher","DOI":"10.1021\/ci034207y"},{"key":"ijcce.2011010106-1","unstructured":"Bisson,G.(2000). La similarit\u00e9: une notion symbolique\/num\u00e9rique. Apprentissage symbolique-num\u00e9rique."},{"key":"ijcce.2011010106-2","doi-asserted-by":"publisher","DOI":"10.1021\/ci00021a011"},{"key":"ijcce.2011010106-3","author":"M. A.Johnson","year":"1990","journal-title":"Concepts and Applications of Molecular Similarity"},{"key":"ijcce.2011010106-4","doi-asserted-by":"publisher","DOI":"10.1021\/ci950274j"},{"key":"ijcce.2011010106-5","doi-asserted-by":"publisher","DOI":"10.1007\/BF00337288"},{"key":"ijcce.2011010106-6","author":"P.Legendre","year":"1998","journal-title":"Numerical ecology"},{"key":"ijcce.2011010106-7","unstructured":"Mah\u00e9, P., & Vert, J. P. (2007). Virtual screening with support vector machines and structure kernels (Tech. Rep. HAL-00166188). Paris: Ecole des Mines de Paris, Centre de Bioinformatique."},{"key":"ijcce.2011010106-8","first-page":"7","article-title":"Introduction to Similarity Searching in Chemistry.","volume":"51","author":"V.Monev","year":"2004","journal-title":"Match-Communications in Mathematical and in Computer Chemistry"},{"key":"ijcce.2011010106-9","unstructured":"Mozziconacci, J. C. (2003). D\u00e9veloppement et application de m\u00e9thodes de drug design: Combinaison des approches de docking-scoring et de QSAR. Unpublished doctoral dissertation, Orleans University, Orleans."},{"key":"ijcce.2011010106-10","author":"C. H.Schwab","year":"2006","journal-title":"AdrianaCode software version 2.0"},{"key":"ijcce.2011010106-11","doi-asserted-by":"publisher","DOI":"10.1016\/S1359-6446(02)02411-X"},{"key":"ijcce.2011010106-12","doi-asserted-by":"crossref","DOI":"10.1002\/9783527613106","author":"R.Todeschini","year":"2000","journal-title":"Handbook of Molecular Descriptors"},{"key":"ijcce.2011010106-13","unstructured":"Todeschini, R., Consonni, V., Mauri, A., & Pavan, M. (2005). Dragon software version 5.3."},{"issue":"3","key":"ijcce.2011010106-14","first-page":"192","article-title":"ADMET in silico modelling: towards prediction paradise?","volume":"2","author":"H.van de Waterbeemd","year":"2003","journal-title":"Journal of Chemical Information and Computer Sciences"},{"key":"ijcce.2011010106-15","doi-asserted-by":"publisher","DOI":"10.1021\/ci9800211"}],"container-title":["International Journal of Chemoinformatics and Chemical Engineering"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=50473","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T23:32:34Z","timestamp":1654126354000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/ijcce.2011010106"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2011,1,1]]},"references-count":16,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2011,1]]}},"URL":"https:\/\/doi.org\/10.4018\/ijcce.2011010106","relation":{},"ISSN":["2155-4110","2155-4129"],"issn-type":[{"value":"2155-4110","type":"print"},{"value":"2155-4129","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,1,1]]}}}