{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T06:10:31Z","timestamp":1700287831537},"reference-count":1,"publisher":"Walter de Gruyter GmbH","issue":"3","license":[{"start":{"date-parts":[[2011,12,1]],"date-time":"2011-12-01T00:00:00Z","timestamp":1322697600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,12,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p> It has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current \u201ctarget-rich, lead-poor\u201d scenario.<\/jats:p><jats:p>Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal - iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms.<\/jats:p>","DOI":"10.1515\/jib-2011-182","type":"journal-article","created":{"date-parts":[[2017,12,7]],"date-time":"2017-12-07T17:01:00Z","timestamp":1512666060000},"page":"176-194","source":"Crossref","is-referenced-by-count":5,"title":["A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies"],"prefix":"10.1515","volume":"8","author":[{"given":"Rui","family":"Camacho","sequence":"first","affiliation":[{"name":"1LIAAD-INESC Porto LA & DEI, FEUP, Universidade do Porto, http:\/\/www.liaad.up.pt\/, Portugal"}]},{"given":"Max","family":"Pereira","sequence":"additional","affiliation":[{"name":"1LIAAD-INESC Porto LA & DEI, FEUP, Universidade do Porto, http:\/\/www.liaad.up.pt\/, Portugal"}]},{"given":"V\u00edtor Santos","family":"Costa","sequence":"additional","affiliation":[{"name":"2CRACS-INESC Porto, http:\/\/cracs.fc.up.pt\/, Portugal"},{"name":"3DCC\/FCUP Universidade do Porto, http:\/\/www.dcc.fc.up.pt\/, Portugal"}]},{"given":"Nuno A.","family":"Fonseca","sequence":"additional","affiliation":[{"name":"2CRACS-INESC Porto, http:\/\/cracs.fc.up.pt\/, Portugal"}]},{"given":"Carlos","family":"Adriano","sequence":"additional","affiliation":[{"name":"1LIAAD-INESC Porto LA & DEI, FEUP, Universidade do Porto, http:\/\/www.liaad.up.pt\/, Portugal"}]},{"given":"Carlos J. V.","family":"Sim\u00f5es","sequence":"additional","affiliation":[{"name":"4Center for Neuroscience and Cell Biology (CNC), University of Coimbra, http:\/\/www.cnbc.pt\/, Portugal"},{"name":"5Chemistry Department, University of Coimbra, http:\/\/www.uc.pt\/fctuc\/dquimica\/, Portugal"}]},{"given":"Rui M. M.","family":"Brito","sequence":"additional","affiliation":[{"name":"4Center for Neuroscience and Cell Biology (CNC), University of Coimbra, http:\/\/www.cnbc.pt\/, Portugal"},{"name":"5Chemistry Department, University of Coimbra, http:\/\/www.uc.pt\/fctuc\/dquimica\/, Portugal"}]}],"member":"374","reference":[{"key":"ref501","first-page":"182","article-title":"by - nd Journal of Integrative Bioinformatics http imbio de hler Biochemical pharma - cology of the atypical neuroleptic remoxipride Computational and Structural Approaches to Drug Discovery Cambridge : Royal Society of Chemistry op yr ig ht Th e A ut ho","volume":"8","author":"Ko","year":"2011","journal-title":"journal Acta Psychiatr Scand"}],"container-title":["Journal of Integrative Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/jib\/8\/3\/article-p176.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/jib-2011-182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T01:56:21Z","timestamp":1619056581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/jib-2011-182\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,12,1]]},"references-count":1,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1515\/jib-2011-182","relation":{},"ISSN":["1613-4516"],"issn-type":[{"value":"1613-4516","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,12,1]]}}}