In silico methods and tools for drug discovery
DC Field | Value | Language |
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dc.contributor.author | Shaker, Bilal | - |
dc.contributor.author | Ahmad, Sajjad | - |
dc.contributor.author | Lee, Jingyu | - |
dc.contributor.author | Jung, Chanjin | - |
dc.contributor.author | Na, Dokyun | - |
dc.date.accessioned | 2021-09-28T09:40:05Z | - |
dc.date.available | 2021-09-28T09:40:05Z | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 0010-4825 | - |
dc.identifier.issn | 1879-0534 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/49956 | - |
dc.description.abstract | In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods. © 2021 Elsevier Ltd | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier Ltd | - |
dc.title | In silico methods and tools for drug discovery | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.compbiomed.2021.104851 | - |
dc.identifier.bibliographicCitation | Computers in Biology and Medicine, v.137 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000703505200002 | - |
dc.identifier.scopusid | 2-s2.0-85114694402 | - |
dc.citation.title | Computers in Biology and Medicine | - |
dc.citation.volume | 137 | - |
dc.type.docType | Article | - |
dc.publisher.location | 영국 | - |
dc.subject.keywordAuthor | Computational drug discovery | - |
dc.subject.keywordAuthor | Computer-aided drug design | - |
dc.subject.keywordAuthor | Target identification | - |
dc.subject.keywordAuthor | Toxicity prediction | - |
dc.subject.keywordAuthor | Virtual screening | - |
dc.subject.keywordPlus | Clinical trial | - |
dc.subject.keywordPlus | Computational drug discovery | - |
dc.subject.keywordPlus | Computer aided drug design | - |
dc.subject.keywordPlus | Conventional drugs | - |
dc.subject.keywordPlus | Drug discovery | - |
dc.subject.keywordPlus | In-silico | - |
dc.subject.keywordPlus | Lead identification | - |
dc.subject.keywordPlus | Target's identifications | - |
dc.subject.keywordPlus | Toxicity predictions | - |
dc.subject.keywordPlus | Virtual Screening | - |
dc.subject.keywordPlus | Computational methods | - |
dc.relation.journalResearchArea | Life Sciences & Biomedicine - Other Topics | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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