Matches in SemOpenAlex for { <https://semopenalex.org/work/W3213493452> ?p ?o ?g. }
- W3213493452 endingPage "1107" @default.
- W3213493452 startingPage "1099" @default.
- W3213493452 abstract "The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost-friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline. Several research teams have developed successful AI platforms for hit identification, lead generation, and lead optimization. In this review, we investigate the technologies at the forefront of spearheading an AI revolution in drug discovery and pharmaceutical sciences." @default.
- W3213493452 created "2021-11-22" @default.
- W3213493452 creator A5006507316 @default.
- W3213493452 creator A5029153470 @default.
- W3213493452 creator A5031411717 @default.
- W3213493452 creator A5033579800 @default.
- W3213493452 creator A5053646200 @default.
- W3213493452 creator A5065425981 @default.
- W3213493452 creator A5068952517 @default.
- W3213493452 creator A5082521701 @default.
- W3213493452 date "2022-04-01" @default.
- W3213493452 modified "2023-10-10" @default.
- W3213493452 title "Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases" @default.
- W3213493452 cites W1501531009 @default.
- W3213493452 cites W1968319881 @default.
- W3213493452 cites W2003405209 @default.
- W3213493452 cites W2013023084 @default.
- W3213493452 cites W2097834518 @default.
- W3213493452 cites W2108069034 @default.
- W3213493452 cites W2135732933 @default.
- W3213493452 cites W2138111780 @default.
- W3213493452 cites W2158310952 @default.
- W3213493452 cites W2166550586 @default.
- W3213493452 cites W2172154910 @default.
- W3213493452 cites W2189911347 @default.
- W3213493452 cites W2204695023 @default.
- W3213493452 cites W2257979135 @default.
- W3213493452 cites W2261254692 @default.
- W3213493452 cites W2294859034 @default.
- W3213493452 cites W2297106365 @default.
- W3213493452 cites W2465613542 @default.
- W3213493452 cites W2470394683 @default.
- W3213493452 cites W2520543291 @default.
- W3213493452 cites W2558748708 @default.
- W3213493452 cites W2602318385 @default.
- W3213493452 cites W2624282460 @default.
- W3213493452 cites W2736137960 @default.
- W3213493452 cites W2767891136 @default.
- W3213493452 cites W2772693146 @default.
- W3213493452 cites W2805404971 @default.
- W3213493452 cites W2895969956 @default.
- W3213493452 cites W2900569176 @default.
- W3213493452 cites W2918544128 @default.
- W3213493452 cites W2919115771 @default.
- W3213493452 cites W2943422424 @default.
- W3213493452 cites W2963058055 @default.
- W3213493452 cites W2963845150 @default.
- W3213493452 cites W2965708760 @default.
- W3213493452 cites W2970137004 @default.
- W3213493452 cites W2979826702 @default.
- W3213493452 cites W2988992984 @default.
- W3213493452 cites W2995523160 @default.
- W3213493452 cites W3004919484 @default.
- W3213493452 cites W3005364306 @default.
- W3213493452 cites W3008526620 @default.
- W3213493452 cites W3023371261 @default.
- W3213493452 cites W3026998947 @default.
- W3213493452 cites W3045354910 @default.
- W3213493452 cites W3090021657 @default.
- W3213493452 cites W3094089973 @default.
- W3213493452 cites W3096828292 @default.
- W3213493452 cites W3096831136 @default.
- W3213493452 cites W3098078680 @default.
- W3213493452 cites W3099910796 @default.
- W3213493452 cites W3112541288 @default.
- W3213493452 cites W3113150977 @default.
- W3213493452 cites W3125539506 @default.
- W3213493452 cites W3126968706 @default.
- W3213493452 cites W3134123677 @default.
- W3213493452 cites W3136264809 @default.
- W3213493452 cites W3177828909 @default.
- W3213493452 cites W4210257598 @default.
- W3213493452 cites W4248107770 @default.
- W3213493452 doi "https://doi.org/10.1016/j.drudis.2021.10.022" @default.
- W3213493452 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8570449" @default.
- W3213493452 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34748992" @default.
- W3213493452 hasPublicationYear "2022" @default.
- W3213493452 type Work @default.
- W3213493452 sameAs 3213493452 @default.
- W3213493452 citedByCount "3" @default.
- W3213493452 countsByYear W32134934522022 @default.
- W3213493452 crossrefType "journal-article" @default.
- W3213493452 hasAuthorship W3213493452A5006507316 @default.
- W3213493452 hasAuthorship W3213493452A5029153470 @default.
- W3213493452 hasAuthorship W3213493452A5031411717 @default.
- W3213493452 hasAuthorship W3213493452A5033579800 @default.
- W3213493452 hasAuthorship W3213493452A5053646200 @default.
- W3213493452 hasAuthorship W3213493452A5065425981 @default.
- W3213493452 hasAuthorship W3213493452A5068952517 @default.
- W3213493452 hasAuthorship W3213493452A5082521701 @default.
- W3213493452 hasBestOaLocation W32134934521 @default.
- W3213493452 hasConcept C112930515 @default.
- W3213493452 hasConcept C116834253 @default.
- W3213493452 hasConcept C142724271 @default.
- W3213493452 hasConcept C2522767166 @default.
- W3213493452 hasConcept C2779134260 @default.
- W3213493452 hasConcept C3008058167 @default.
- W3213493452 hasConcept C41008148 @default.