FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4380714780> ?p ?o ?g. }

• W4380714780 endingPage "3730" @default.
• W4380714780 startingPage "3719" @default.
• W4380714780 abstract "Deep learning generative models are now being applied in various fields including drug discovery. In this work, we propose a novel approach to include target 3D structural information in molecular generative models for structure-based drug design. The method combines a message-passing neural network model that predicts docking scores with a generative neural network model as its reward function to navigate the chemical space searching for molecules that bind favorably with a specific target. A key feature of the method is the construction of target-specific molecular sets for training, designed to overcome potential transferability issues of surrogate docking models through a two-round training process. Consequently, this enables accurate guided exploration of the chemical space without reliance on the collection of prior knowledge about active and inactive compounds for the specific target. Tests on eight target proteins showed a 100-fold increase in hit generation compared to conventional docking calculations and the ability to generate molecules similar to approved drugs or known active ligands for specific targets without prior knowledge. This method provides a general and highly efficient solution for structure-based molecular generation." @default.
• W4380714780 created "2023-06-15" @default.
• W4380714780 creator A5025284426 @default.
• W4380714780 creator A5026817460 @default.
• W4380714780 creator A5060822545 @default.
• W4380714780 date "2023-06-15" @default.
• W4380714780 modified "2023-10-17" @default.
• W4380714780 title "A Simple Way to Incorporate Target Structural Information in Molecular Generative Models" @default.
• W4380714780 cites W1966613725 @default.
• W4380714780 cites W1997176810 @default.
• W4380714780 cites W2017254234 @default.
• W4380714780 cites W2042572511 @default.
• W4380714780 cites W2066273100 @default.
• W4380714780 cites W2067643341 @default.
• W4380714780 cites W2105668062 @default.
• W4380714780 cites W2108389384 @default.
• W4380714780 cites W2128332459 @default.
• W4380714780 cites W2134967712 @default.
• W4380714780 cites W2151971404 @default.
• W4380714780 cites W2334483166 @default.
• W4380714780 cites W2388080941 @default.
• W4380714780 cites W2610148085 @default.
• W4380714780 cites W2765224015 @default.
• W4380714780 cites W2789748145 @default.
• W4380714780 cites W2900090807 @default.
• W4380714780 cites W2912171584 @default.
• W4380714780 cites W2966357564 @default.
• W4380714780 cites W2989615256 @default.
• W4380714780 cites W2991736596 @default.
• W4380714780 cites W3020511336 @default.
• W4380714780 cites W3027975282 @default.
• W4380714780 cites W3029978182 @default.
• W4380714780 cites W3090915092 @default.
• W4380714780 cites W3092297806 @default.
• W4380714780 cites W3094640617 @default.
• W4380714780 cites W3094686696 @default.
• W4380714780 cites W3100751385 @default.
• W4380714780 cites W3106761016 @default.
• W4380714780 cites W3109549311 @default.
• W4380714780 cites W3112474878 @default.
• W4380714780 cites W3125566655 @default.
• W4380714780 cites W3133989558 @default.
• W4380714780 cites W3135642867 @default.
• W4380714780 cites W3144567229 @default.
• W4380714780 cites W3162583919 @default.
• W4380714780 cites W3174777205 @default.
• W4380714780 cites W3200806939 @default.
• W4380714780 cites W3201539868 @default.
• W4380714780 cites W3209056694 @default.
• W4380714780 cites W3209118444 @default.
• W4380714780 cites W3209764902 @default.
• W4380714780 cites W3212070798 @default.
• W4380714780 cites W4200150297 @default.
• W4380714780 cites W4200233821 @default.
• W4380714780 cites W4210589602 @default.
• W4380714780 cites W4210617107 @default.
• W4380714780 cites W4221054065 @default.
• W4380714780 cites W4296794490 @default.
• W4380714780 cites W4297853666 @default.
• W4380714780 cites W4297995910 @default.
• W4380714780 cites W4304203195 @default.
• W4380714780 cites W4309413039 @default.
• W4380714780 doi "https://doi.org/10.1021/acs.jcim.3c00293" @default.
• W4380714780 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37318828" @default.
• W4380714780 hasPublicationYear "2023" @default.
• W4380714780 type Work @default.
• W4380714780 citedByCount "0" @default.
• W4380714780 crossrefType "journal-article" @default.
• W4380714780 hasAuthorship W4380714780A5025284426 @default.
• W4380714780 hasAuthorship W4380714780A5026817460 @default.
• W4380714780 hasAuthorship W4380714780A5060822545 @default.
• W4380714780 hasBestOaLocation W43807147802 @default.
• W4380714780 hasConcept C119857082 @default.
• W4380714780 hasConcept C140331021 @default.
• W4380714780 hasConcept C154945302 @default.
• W4380714780 hasConcept C159110408 @default.
• W4380714780 hasConcept C167966045 @default.
• W4380714780 hasConcept C185592680 @default.
• W4380714780 hasConcept C2989108626 @default.
• W4380714780 hasConcept C39890363 @default.
• W4380714780 hasConcept C41008148 @default.
• W4380714780 hasConcept C41685203 @default.
• W4380714780 hasConcept C50644808 @default.
• W4380714780 hasConcept C51632099 @default.
• W4380714780 hasConcept C55493867 @default.
• W4380714780 hasConcept C60644358 @default.
• W4380714780 hasConcept C61272859 @default.
• W4380714780 hasConcept C70721500 @default.
• W4380714780 hasConcept C71924100 @default.
• W4380714780 hasConcept C74187038 @default.
• W4380714780 hasConcept C86803240 @default.
• W4380714780 hasConcept C99726746 @default.
• W4380714780 hasConceptScore W4380714780C119857082 @default.
• W4380714780 hasConceptScore W4380714780C140331021 @default.
• W4380714780 hasConceptScore W4380714780C154945302 @default.
• W4380714780 hasConceptScore W4380714780C159110408 @default.
• W4380714780 hasConceptScore W4380714780C167966045 @default.
• W4380714780 hasConceptScore W4380714780C185592680 @default.

• next