{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/33363"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/33363","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"Virtual Screening of Multi-Target Agents by Combinatorial Machine Learning Methods","abstract":"Multi-target drugs have greatly attracted the attention and interest in drug discovery. As a joint effort, the Kinetics database of biomolecular interactions and the Therapeutic targets database were upgraded. They can offer informative data in multi-target drug discovery. I explored combinatorial support vector machines (COMBI-SVM) tool for virtual screening of multi-target agents. After the preliminarily tests of COMBI-SVMs for 4 dual-kinase inhibitors pairs (EGFR-Src, EGFR-FGFR, VEGFR-Lck, Src-Lck), I applied the COMBI-SVMs to the identification of dual-target antidepressant agents of 7 target combinations (serotonin transporter paired with noradrenaline transporter, H3 receptor, 5-HT1A receptor, 5-HT1B receptor, 5-HT2C receptor, Melanocortin 4 receptor and Neurokinin 1 receptor respectively). COMBI-SVMs were compared to other VS methods in varies testing sets (e.g. MDDR and PubChem databases). They showed comparable dual-inhibitor yields, moderate to good target selectivity in misidentifying individual-target inhibitors of the same target pair and inhibitors of the other target pairs as dual-inhibitors, low dual-inhibitor false-hit rates in screening large databases MDDR and PubChem.","abstract_html":"Multi-target drugs have greatly attracted the attention and interest in drug discovery. As a joint effort, the Kinetics database of biomolecular interactions and the Therapeutic targets database were upgraded. They can offer informative data in multi-target drug discovery. I explored combinatorial support vector machines (COMBI-SVM) tool for virtual screening of multi-target agents. After the preliminarily tests of COMBI-SVMs for 4 dual-kinase inhibitors pairs (EGFR-Src, EGFR-FGFR, VEGFR-Lck, Src-Lck), I applied the COMBI-SVMs to the identification of dual-target antidepressant agents of 7 target combinations (serotonin transporter paired with noradrenaline transporter, H3 receptor, 5-HT1A receptor, 5-HT1B receptor, 5-HT2C receptor, Melanocortin 4 receptor and Neurokinin 1 receptor respectively). COMBI-SVMs were compared to other VS methods in varies testing sets (e.g. MDDR and PubChem databases). They showed comparable dual-inhibitor yields, moderate to good target selectivity in misidentifying individual-target inhibitors of the same target pair and inhibitors of the other target pairs as dual-inhibitors, low dual-inhibitor false-hit rates in screening large databases MDDR and PubChem.","abstract_has_math":false,"creators":["SHI ZHE"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-09-13","date_published":"2011-09-13","updated_at":"2026-07-24T03:31:13Z","subjects":["virtual screening, multi-target, machine learning methods"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["SHI ZHE"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2011-09-13"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://scholarbank.nus.edu.sg/handle/10635/33363"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["virtual screening, multi-target, machine learning methods"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarbank.nus.edu.sg/bitstreams/c9fbb7d5-8606-4ef9-917d-d31ba10ce4a6/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Multi-target drugs have greatly attracted the attention and interest in drug discovery. As a joint effort, the Kinetics database of biomolecular interactions and the Therapeutic targets database were upgraded. They can offer informative data in multi-target drug discovery. I explored combinatorial support vector machines (COMBI-SVM) tool for virtual screening of multi-target agents. After the preliminarily tests of COMBI-SVMs for 4 dual-kinase inhibitors pairs (EGFR-Src, EGFR-FGFR, VEGFR-Lck, Src-Lck), I applied the COMBI-SVMs to the identification of dual-target antidepressant agents of 7 target combinations (serotonin transporter paired with noradrenaline transporter, H3 receptor, 5-HT1A receptor, 5-HT1B receptor, 5-HT2C receptor, Melanocortin 4 receptor and Neurokinin 1 receptor respectively). COMBI-SVMs were compared to other VS methods in varies testing sets (e.g. MDDR and PubChem databases). They showed comparable dual-inhibitor yields, moderate to good target selectivity in misidentifying individual-target inhibitors of the same target pair and inhibitors of the other target pairs as dual-inhibitors, low dual-inhibitor false-hit rates in screening large databases MDDR and PubChem."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["8a8c54dba61b232d523829832a128cfc","15d0f2098942bebb9a697c4257b2a2fe"]},{"key":"dc:title","label":"Title","values":["Virtual Screening of Multi-Target Agents by Combinatorial Machine Learning Methods"]}]}],"canonical_facts":{"dc:creator":["SHI ZHE"],"dc:date.issued":["2011-09-13"],"dc:description.abstract":["Multi-target drugs have greatly attracted the attention and interest in drug discovery. As a joint effort, the Kinetics database of biomolecular interactions and the Therapeutic targets database were upgraded. They can offer informative data in multi-target drug discovery. 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