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10.1177/11779322211030364 http://scihub22266oqcxt.onion/10.1177/11779322211030364 34290496!8274096!34290496     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinform+Biol+Insights 2021 ; 15 (ä): 11779322211030364 Nephropedia Template TP {{tp|p=34290496|t=2021. DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity |pdf=|usr=}}{{34290496}} gab.com Text DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity JO: Bioinform Biol Insights http://www.kidney.de/pget.php?&tw=1&pmid=34290496 #FOAvip Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to "learn" intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (?2.5 A) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes. Twit Text FOAVip DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity ????Bioinform Biol Insights http://www.kidney.de/pget.php?&tw=1&pmid=34290496 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34290496 #FOAvip English Wikipedia <ref>{{Cite pmid|34290496}}</ref> DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity #MMPMID34290496 Ahmed A; Mam B; Sowdhamini R Bioinform Biol Insights 2021[]; 15 (ä): 11779322211030364 PMID34290496show ga Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to "learn" intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (?2.5 A) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes. äDEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Aff(epmc).pdf DeepDyve Pubget Overpricing