Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313256479> ?p ?o ?g. }
- W4313256479 endingPage "105892" @default.
- W4313256479 startingPage "105892" @default.
- W4313256479 abstract "Accurate prediction of protein-ligand binding affinity is crucial in structure-based drug design but remains some challenges even with recent advances in deep learning: (1) Existing methods neglect the edge information in protein and ligand structure data; (2) current attention mechanisms struggle to capture true binding interactions in the small dataset. Herein, we proposed SEGSA_DTA, a SuperEdge Graph convolution-based and Supervised Attention-based Drug-Target Affinity prediction method, where the super edge graph convolution can comprehensively utilize node and edge information and the multi-supervised attention module can efficiently learn the attention distribution consistent with real protein-ligand interactions. Results on the multiple datasets show that SEGSA_DTA outperforms current state-of-the-art methods. We also applied SEGSA_DTA in repurposing FDA-approved drugs to identify potential coronavirus disease 2019 (COVID-19) treatments. Besides, by using SHapley Additive exPlanations (SHAP), we found that SEGSA_DTA is interpretable and further provides a new quantitative analytical solution for structure-based lead optimization." @default.
- W4313256479 created "2023-01-06" @default.
- W4313256479 creator A5005397325 @default.
- W4313256479 creator A5019112889 @default.
- W4313256479 creator A5023022034 @default.
- W4313256479 creator A5023022659 @default.
- W4313256479 creator A5045570568 @default.
- W4313256479 creator A5047307778 @default.
- W4313256479 creator A5051254107 @default.
- W4313256479 creator A5052890986 @default.
- W4313256479 creator A5078163331 @default.
- W4313256479 creator A5086685216 @default.
- W4313256479 date "2023-01-01" @default.
- W4313256479 modified "2023-10-14" @default.
- W4313256479 title "Protein–ligand binding affinity prediction with edge awareness and supervised attention" @default.
- W4313256479 cites W141181845 @default.
- W4313256479 cites W1968319881 @default.
- W4313256479 cites W1993046136 @default.
- W4313256479 cites W1996295408 @default.
- W4313256479 cites W2004386051 @default.
- W4313256479 cites W2013085020 @default.
- W4313256479 cites W2025816743 @default.
- W4313256479 cites W2030850720 @default.
- W4313256479 cites W2043338013 @default.
- W4313256479 cites W2051820228 @default.
- W4313256479 cites W2084493621 @default.
- W4313256479 cites W2102377211 @default.
- W4313256479 cites W2109991441 @default.
- W4313256479 cites W2143399539 @default.
- W4313256479 cites W2145446216 @default.
- W4313256479 cites W2418209200 @default.
- W4313256479 cites W2582933288 @default.
- W4313256479 cites W2607560340 @default.
- W4313256479 cites W2750315284 @default.
- W4313256479 cites W2767891136 @default.
- W4313256479 cites W2781821160 @default.
- W4313256479 cites W2785947426 @default.
- W4313256479 cites W2800525511 @default.
- W4313256479 cites W2809216727 @default.
- W4313256479 cites W2860192827 @default.
- W4313256479 cites W2896002881 @default.
- W4313256479 cites W2918239264 @default.
- W4313256479 cites W2945551948 @default.
- W4313256479 cites W2968734407 @default.
- W4313256479 cites W2969325194 @default.
- W4313256479 cites W2978484973 @default.
- W4313256479 cites W3015889210 @default.
- W4313256479 cites W3016983547 @default.
- W4313256479 cites W3019745511 @default.
- W4313256479 cites W3037203467 @default.
- W4313256479 cites W3080418884 @default.
- W4313256479 cites W3096561213 @default.
- W4313256479 cites W3100704554 @default.
- W4313256479 cites W3100786684 @default.
- W4313256479 cites W3104537585 @default.
- W4313256479 cites W3108240937 @default.
- W4313256479 cites W3134958209 @default.
- W4313256479 cites W3148554359 @default.
- W4313256479 cites W3149537966 @default.
- W4313256479 cites W3151148247 @default.
- W4313256479 cites W3164132254 @default.
- W4313256479 cites W3165548596 @default.
- W4313256479 doi "https://doi.org/10.1016/j.isci.2022.105892" @default.
- W4313256479 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36691617" @default.
- W4313256479 hasPublicationYear "2023" @default.
- W4313256479 type Work @default.
- W4313256479 citedByCount "1" @default.
- W4313256479 countsByYear W43132564792023 @default.
- W4313256479 crossrefType "journal-article" @default.
- W4313256479 hasAuthorship W4313256479A5005397325 @default.
- W4313256479 hasAuthorship W4313256479A5019112889 @default.
- W4313256479 hasAuthorship W4313256479A5023022034 @default.
- W4313256479 hasAuthorship W4313256479A5023022659 @default.
- W4313256479 hasAuthorship W4313256479A5045570568 @default.
- W4313256479 hasAuthorship W4313256479A5047307778 @default.
- W4313256479 hasAuthorship W4313256479A5051254107 @default.
- W4313256479 hasAuthorship W4313256479A5052890986 @default.
- W4313256479 hasAuthorship W4313256479A5078163331 @default.
- W4313256479 hasAuthorship W4313256479A5086685216 @default.
- W4313256479 hasBestOaLocation W43132564791 @default.
- W4313256479 hasConcept C103637391 @default.
- W4313256479 hasConcept C116569031 @default.
- W4313256479 hasConcept C118552586 @default.
- W4313256479 hasConcept C119857082 @default.
- W4313256479 hasConcept C124101348 @default.
- W4313256479 hasConcept C132525143 @default.
- W4313256479 hasConcept C13280743 @default.
- W4313256479 hasConcept C154945302 @default.
- W4313256479 hasConcept C15744967 @default.
- W4313256479 hasConcept C162307627 @default.
- W4313256479 hasConcept C170493617 @default.
- W4313256479 hasConcept C185592680 @default.
- W4313256479 hasConcept C185798385 @default.
- W4313256479 hasConcept C18903297 @default.
- W4313256479 hasConcept C205649164 @default.
- W4313256479 hasConcept C2780035454 @default.
- W4313256479 hasConcept C41008148 @default.
- W4313256479 hasConcept C519536355 @default.