Matches in SemOpenAlex for { <https://semopenalex.org/work/W4319778731> ?p ?o ?g. }
- W4319778731 endingPage "35" @default.
- W4319778731 startingPage "19" @default.
- W4319778731 abstract "Drug development is a time-consuming, expensive and extremely risky procedure. Up to 90% of drug concepts are discarded due to challenges such as safety, efficacy and toxicity resulting in significant losses for the investor. The use of artificial intelligence (AI), namely machine learning and deep learning algorithms, to improve the drug discovery process is one technique that has arisen in recent years. AI has been effectively used in drug discovery and design. This chapter includes these machine learning approaches in depth, as well as their applications in medicinal chemistry. The current state-of-the-art of AI supported pharmaceutical discovery is discussed, including applications in structure and ligand-based virtual screening, de novo drug design, drug repurposing, and factors related, after introducing the basic principles, along with some application notes, of the various machine learning algorithms. Finally, obstacles and limits are outlined, with an eye towards possible future avenues for AI-supported drug discovery and design." @default.
- W4319778731 created "2023-02-11" @default.
- W4319778731 creator A5011942447 @default.
- W4319778731 creator A5017972941 @default.
- W4319778731 creator A5045977931 @default.
- W4319778731 creator A5075520218 @default.
- W4319778731 date "2023-02-07" @default.
- W4319778731 modified "2023-10-12" @default.
- W4319778731 title "Artificial Intelligence and Machine Learning‐Based New Drug Discovery Process with Molecular Modelling" @default.
- W4319778731 cites W1786404767 @default.
- W4319778731 cites W2009605433 @default.
- W4319778731 cites W2022796772 @default.
- W4319778731 cites W2028440694 @default.
- W4319778731 cites W2031649959 @default.
- W4319778731 cites W2034549041 @default.
- W4319778731 cites W2039929264 @default.
- W4319778731 cites W2042572511 @default.
- W4319778731 cites W2052030759 @default.
- W4319778731 cites W2090049785 @default.
- W4319778731 cites W2114779636 @default.
- W4319778731 cites W2136945334 @default.
- W4319778731 cites W2147472674 @default.
- W4319778731 cites W2148143831 @default.
- W4319778731 cites W2160592148 @default.
- W4319778731 cites W2287058098 @default.
- W4319778731 cites W2340782621 @default.
- W4319778731 cites W2554099577 @default.
- W4319778731 cites W2565684601 @default.
- W4319778731 cites W2567231876 @default.
- W4319778731 cites W2567534979 @default.
- W4319778731 cites W2801991413 @default.
- W4319778731 cites W2895420596 @default.
- W4319778731 cites W2895896993 @default.
- W4319778731 cites W2901340108 @default.
- W4319778731 cites W2901411193 @default.
- W4319778731 cites W2914907363 @default.
- W4319778731 cites W2916688788 @default.
- W4319778731 cites W2943133682 @default.
- W4319778731 cites W2957366757 @default.
- W4319778731 cites W2959938226 @default.
- W4319778731 cites W2963729354 @default.
- W4319778731 cites W3014689923 @default.
- W4319778731 cites W3022395336 @default.
- W4319778731 cites W3023126697 @default.
- W4319778731 cites W3037826474 @default.
- W4319778731 cites W3043158132 @default.
- W4319778731 cites W3047187489 @default.
- W4319778731 cites W3094492244 @default.
- W4319778731 cites W3097758405 @default.
- W4319778731 cites W3131943919 @default.
- W4319778731 cites W3136689296 @default.
- W4319778731 cites W3143491409 @default.
- W4319778731 cites W3160713245 @default.
- W4319778731 cites W3166218875 @default.
- W4319778731 cites W3173957093 @default.
- W4319778731 cites W3201042237 @default.
- W4319778731 cites W3208344849 @default.
- W4319778731 cites W3210388045 @default.
- W4319778731 cites W4213151958 @default.
- W4319778731 cites W4231109964 @default.
- W4319778731 doi "https://doi.org/10.1002/9781119865728.ch2" @default.
- W4319778731 hasPublicationYear "2023" @default.
- W4319778731 type Work @default.
- W4319778731 citedByCount "1" @default.
- W4319778731 countsByYear W43197787312023 @default.
- W4319778731 crossrefType "other" @default.
- W4319778731 hasAuthorship W4319778731A5011942447 @default.
- W4319778731 hasAuthorship W4319778731A5017972941 @default.
- W4319778731 hasAuthorship W4319778731A5045977931 @default.
- W4319778731 hasAuthorship W4319778731A5075520218 @default.
- W4319778731 hasConcept C103637391 @default.
- W4319778731 hasConcept C103697762 @default.
- W4319778731 hasConcept C108583219 @default.
- W4319778731 hasConcept C111919701 @default.
- W4319778731 hasConcept C119857082 @default.
- W4319778731 hasConcept C127413603 @default.
- W4319778731 hasConcept C154945302 @default.
- W4319778731 hasConcept C2522767166 @default.
- W4319778731 hasConcept C2780035454 @default.
- W4319778731 hasConcept C41008148 @default.
- W4319778731 hasConcept C519536355 @default.
- W4319778731 hasConcept C548081761 @default.
- W4319778731 hasConcept C60644358 @default.
- W4319778731 hasConcept C68762167 @default.
- W4319778731 hasConcept C71924100 @default.
- W4319778731 hasConcept C74187038 @default.
- W4319778731 hasConcept C86803240 @default.
- W4319778731 hasConcept C98045186 @default.
- W4319778731 hasConcept C98274493 @default.
- W4319778731 hasConceptScore W4319778731C103637391 @default.
- W4319778731 hasConceptScore W4319778731C103697762 @default.
- W4319778731 hasConceptScore W4319778731C108583219 @default.
- W4319778731 hasConceptScore W4319778731C111919701 @default.
- W4319778731 hasConceptScore W4319778731C119857082 @default.
- W4319778731 hasConceptScore W4319778731C127413603 @default.
- W4319778731 hasConceptScore W4319778731C154945302 @default.
- W4319778731 hasConceptScore W4319778731C2522767166 @default.
- W4319778731 hasConceptScore W4319778731C2780035454 @default.