Publication
QSAR modeling of antitubercular activity of diverse organic compounds
| Summary: | Tuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved. |
|---|---|
| Main Authors: | Kovalishyn, Vasyl |
| Other Authors: | Aires-de-Sousa, Joao; Ventura, Cristina; Elvas Leitao, Ruben; Martins, Filomena |
| Subject: | QSAR Neural Networks Random Forests Antitubercular Drug Design Neural-Network Antimycobacterial Activity Benzimidazole Derivatives Variable Selection In-Vitro Mycobacterium-Tuberculosis Isoniazid Derivatives Agents Inhibitor Design |
| Year: | 2011 |
| Country: | Portugal |
| Document type: | article |
| Access type: | open access |
| Associated institution: | Instituto Politécnico de Lisboa |
| Language: | English |
| Origin: | Repositório Científico do Instituto Politécnico de Lisboa |
| _version_ | 1863852877356728320 |
|---|---|
| author | Kovalishyn, Vasyl |
| author2 | Aires-de-Sousa, Joao Ventura, Cristina Elvas Leitao, Ruben Martins, Filomena |
| author2_role | author author author author |
| author_facet | Kovalishyn, Vasyl Aires-de-Sousa, Joao Ventura, Cristina Elvas Leitao, Ruben Martins, Filomena |
| author_role | author |
| contributor_name_str_mv | RCIPL |
| country_str | PT |
| creators_json_str | [{\"Person.name\":\"Kovalishyn, Vasyl\",\"Person.identifier.orcid\":\"0000-0002-9352-7332\"},{\"Person.name\":\"Aires-de-Sousa, Joao\",\"Person.identifier.orcid\":\"0000-0002-5887-2966\"},{\"Person.name\":\"Ventura, Cristina\"},{\"Person.name\":\"Elvas Leitao, Ruben\",\"Person.identifier.orcid\":\"0000-0002-2196-412X\"},{\"Person.name\":\"Martins, Filomena\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | RCIPL |
| datacite.creators.creator.creatorName.fl_str_mv | Kovalishyn, Vasyl Aires-de-Sousa, Joao Ventura, Cristina Elvas Leitao, Ruben Martins, Filomena |
| datacite.date.Accepted.fl_str_mv | 2011-05-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2013-02-16T17:50:57Z |
| datacite.date.embargoed.fl_str_mv | 2013-02-16T17:50:57Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | QSAR Neural Networks Random Forests Antitubercular Drug Design Neural-Network Antimycobacterial Activity Benzimidazole Derivatives Variable Selection In-Vitro Mycobacterium-Tuberculosis Isoniazid Derivatives Agents Inhibitor Design |
| datacite.titles.title.fl_str_mv | QSAR modeling of antitubercular activity of diverse organic compounds |
| dc.contributor.none.fl_str_mv | RCIPL |
| dc.creator.none.fl_str_mv | Kovalishyn, Vasyl Aires-de-Sousa, Joao Ventura, Cristina Elvas Leitao, Ruben Martins, Filomena |
| dc.date.Accepted.fl_str_mv | 2011-05-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2013-02-16T17:50:57Z |
| dc.date.embargoed.fl_str_mv | 2013-02-16T17:50:57Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10400.21/2232 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Elsevier Science BV |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | QSAR Neural Networks Random Forests Antitubercular Drug Design Neural-Network Antimycobacterial Activity Benzimidazole Derivatives Variable Selection In-Vitro Mycobacterium-Tuberculosis Isoniazid Derivatives Agents Inhibitor Design |
| dc.title.fl_str_mv | QSAR modeling of antitubercular activity of diverse organic compounds |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Tuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorio.ipl.pt/bitstreams/7d3cbfec-b442-45b8-9857-0116662bb0ec/download |
| id | ripl_90c809967fc07ab83e21ad8b364ff35c |
| identifier.url.fl_str_mv | http://hdl.handle.net/10400.21/2232 |
| instacron_str | ipl |
| institution | Instituto Politécnico de Lisboa |
| instname_str | Instituto Politécnico de Lisboa |
| language | eng |
| network_acronym_str | ripl |
| network_name_str | Repositório Científico do Instituto Politécnico de Lisboa |
| oai_identifier_str | oai:repositorio.ipl.pt:10400.21/2232 |
| organization_str_mv | urn:organizationAcronym:ipl |
| person_str_mv | Kovalishyn, Vasyl Kovalishyn, Vasyl http://orcid.org/0000-0002-9352-7332 0000-0002-9352-7332 Aires-de-Sousa, Joao Aires-de-Sousa, Joao https://www.ciencia-id.pt/171B-1434-6FC1 171B-1434-6FC1 http://orcid.org/0000-0002-5887-2966 0000-0002-5887-2966 Ventura, Cristina Elvas Leitao, Ruben Elvas Leitao, Ruben https://www.ciencia-id.pt/F41B-8A26-88D4 F41B-8A26-88D4 http://orcid.org/0000-0002-2196-412X 0000-0002-2196-412X Martins, Filomena |
| publishDate | 2011 |
| publisher.none.fl_str_mv | Elsevier Science BV |
| reponame_str | Repositório Científico do Instituto Politécnico de Lisboa |
| repository_id_str | urn:repositoryAcronym:ripl |
| service_str_mv | urn:repositoryAcronym:ripl |
| spelling | engElsevier Science BVporTuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved.application/pdfporQSAR modeling of antitubercular activity of diverse organic compoundsPersonalKovalishyn, VasylDSpacehttp://dspace.org/items/9df4c185-2270-47af-8813-c18428b5498dDSpacehttp://dspace.org/items/9df4c185-2270-47af-8813-c18428b5498dKovalishynVasylORCIDhttp://orcid.org0000-0002-9352-7332Researcher IDhttps://www.researcherid.comI-6823-2018Scopus Author IDhttps://www.scopus.com56160092400PersonalAires-de-Sousa, JoaoDSpacehttp://dspace.org/items/6662c1f3-0ccf-4f2c-9b34-81229d7b09dbDSpacehttp://dspace.org/items/6662c1f3-0ccf-4f2c-9b34-81229d7b09dbAires-de-SousaJoaoCiência IDhttps://www.ciencia-id.pt171B-1434-6FC1ORCIDhttp://orcid.org0000-0002-5887-2966Researcher IDhttps://www.researcherid.comC-7826-2013Scopus Author IDhttps://www.scopus.com6603089025Ventura, CristinaPersonalElvas Leitao, RubenDSpacehttp://dspace.org/items/440b7129-d4d9-4225-a936-ca694c0984b6DSpacehttp://dspace.org/items/440b7129-d4d9-4225-a936-ca694c0984b6Elvas LeitaoRubenCiência IDhttps://www.ciencia-id.ptF41B-8A26-88D4ORCIDhttp://orcid.org0000-0002-2196-412XResearcher IDhttps://www.researcherid.comD-2452-2009Scopus Author IDhttps://www.scopus.com55667178200Martins, FilomenaHostingInstitutionOrganizationalRCIPLe-mailmailto:rcaap@sp.ipl.ptrcaap@sp.ipl.ptISSNIsPartOf0169-74392013-02-16T17:50:57Z2011-052011-05-01T00:00:00ZHandlehttp://hdl.handle.net/10400.21/2232http://purl.org/coar/access_right/c_abf2open accessQSARNeural NetworksRandom ForestsAntitubercularDrug DesignNeural-NetworkAntimycobacterial ActivityBenzimidazole DerivativesVariable SelectionIn-VitroMycobacterium-TuberculosisIsoniazid DerivativesAgentsInhibitorDesign280502 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.ipl.pt/bitstreams/7d3cbfec-b442-45b8-9857-0116662bb0ec/downloadChemometrics and Intelligent Laboratory Systems10716974Amsterdam |
| spellingShingle | QSAR modeling of antitubercular activity of diverse organic compounds Kovalishyn, Vasyl QSAR Neural Networks Random Forests Antitubercular Drug Design Neural-Network Antimycobacterial Activity Benzimidazole Derivatives Variable Selection In-Vitro Mycobacterium-Tuberculosis Isoniazid Derivatives Agents Inhibitor Design |
| subject.fl_str_mv | QSAR Neural Networks Random Forests Antitubercular Drug Design Neural-Network Antimycobacterial Activity Benzimidazole Derivatives Variable Selection In-Vitro Mycobacterium-Tuberculosis Isoniazid Derivatives Agents Inhibitor Design |
| title | QSAR modeling of antitubercular activity of diverse organic compounds |
| title_full | QSAR modeling of antitubercular activity of diverse organic compounds |
| title_fullStr | QSAR modeling of antitubercular activity of diverse organic compounds |
| title_full_unstemmed | QSAR modeling of antitubercular activity of diverse organic compounds |
| title_short | QSAR modeling of antitubercular activity of diverse organic compounds |
| title_sort | QSAR modeling of antitubercular activity of diverse organic compounds |
| topic | QSAR Neural Networks Random Forests Antitubercular Drug Design Neural-Network Antimycobacterial Activity Benzimidazole Derivatives Variable Selection In-Vitro Mycobacterium-Tuberculosis Isoniazid Derivatives Agents Inhibitor Design |
| topic_facet | QSAR Neural Networks Random Forests Antitubercular Drug Design Neural-Network Antimycobacterial Activity Benzimidazole Derivatives Variable Selection In-Vitro Mycobacterium-Tuberculosis Isoniazid Derivatives Agents Inhibitor Design |
| url | http://hdl.handle.net/10400.21/2232 |
| visible | 1 |