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10.1093/bib/bbab117 http://scihub22266oqcxt.onion/10.1093/bib/bbab117 33866349!8083346!33866349     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\33866349.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Brief+Bioinform 2021 ; 22 (5): ä Nephropedia Template TP {{tp|p=33866349|t=2021. Deep drug-target binding affinity prediction with multiple attention blocks |pdf=|usr=}}{{33866349}} gab.com Text Deep drug-target binding affinity prediction with multiple attention blocks JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33866349 #FOAvip Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation. Based on those observations, in this study, we propose an end-to-end model with multiple attention blocks to predict the binding affinity scores of drug-target pairs. Our proposed model offers the abilities to (i) encode the correlations between atoms by a relation-aware self-attention block and (ii) model the interaction of drug representations and target representations by the multi-head attention block. Experimental results of DTI prediction on two benchmark datasets show our approach outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block. Moreover, we conduct the experiments on the effects of max relative position length and find out the best max relative position length value $k \in \3, 5\$. Furthermore, we apply our model to predict the binding affinity of Corona Virus Disease 2019 (COVID-19)-related genome sequences and $3137$ FDA-approved drugs. Twit Text FOAVip Deep drug-target binding affinity prediction with multiple attention blocks ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33866349 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33866349 #FOAvip English Wikipedia <ref>{{Cite pmid|33866349}}</ref> Deep drug-target binding affinity prediction with multiple attention blocks #MMPMID33866349 Zeng Y; Chen X; Luo Y; Li X; Peng D Brief Bioinform 2021[Sep]; 22 (5): ä PMID33866349show ga Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation. Based on those observations, in this study, we propose an end-to-end model with multiple attention blocks to predict the binding affinity scores of drug-target pairs. Our proposed model offers the abilities to (i) encode the correlations between atoms by a relation-aware self-attention block and (ii) model the interaction of drug representations and target representations by the multi-head attention block. Experimental results of DTI prediction on two benchmark datasets show our approach outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block. Moreover, we conduct the experiments on the effects of max relative position length and find out the best max relative position length value $k \in \3, 5\$. Furthermore, we apply our model to predict the binding affinity of Corona Virus Disease 2019 (COVID-19)-related genome sequences and $3137$ FDA-approved drugs. |*Drug Delivery Systems[MESH] |Algorithms[MESH] |Binding Sites[MESH] |COVID-19 Drug Treatment[MESH] |COVID-19/virology[MESH] |Deep Learning[MESH] |Humans[MESH]Deep drug-target binding affinity prediction with multiple attention b(epmc).pdf DeepDyve Pubget Overpricing