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10.3389/frai.2020.00065 http://scihub22266oqcxt.onion/10.3389/frai.2020.00065 33733182!7861281!33733182     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Artif+Intell 2020 ; 3 (ä): 65 Nephropedia Template TP {{tp|p=33733182|t=2020. Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development |pdf=|usr=}}{{33733182}} gab.com Text Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development JO: Front Artif Intell http://www.kidney.de/pget.php?&tw=1&pmid=33733182 #FOAvip SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies. Twit Text FOAVip Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development ????Front Artif Intell http://www.kidney.de/pget.php?&tw=1&pmid=33733182 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33733182 #FOAvip English Wikipedia <ref>{{Cite pmid|33733182}}</ref> Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development #MMPMID33733182 Keshavarzi Arshadi A; Webb J; Salem M; Cruz E; Calad-Thomson S; Ghadirian N; Collins J; Diez-Cecilia E; Kelly B; Goodarzi H; Yuan JS Front Artif Intell 2020[]; 3 (ä): 65 PMID33733182show ga SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies. äArtificial Intelligence for COVID-19 Drug Discovery and Vaccine Develo(epmc).pdf DeepDyve Pubget Overpricing