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10.1016/j.jbi.2021.103821 http://scihub22266oqcxt.onion/10.1016/j.jbi.2021.103821 34052441!8159673!34052441     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 280.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Biomed+Inform 2021 ; 119 (ä): 103821 Nephropedia Template TP {{tp|p=34052441|t=2021. Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations |pdf=|usr=}}{{34052441}} gab.com Text Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations JO: J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=34052441 #FOAvip AIM: Rapidly developing AI and machine learning (ML) technologies can expedite therapeutic development and in the time of current pandemic their merits are particularly in focus. The purpose of this study was to explore various ML approaches for molecular property prediction and illustrate their utility for identifying potential SARS-CoV-2 3CLpro inhibitors. MATERIALS AND METHODS: We perform a series of drug discovery screenings based on supervised ML models operating in different ways on molecular representations, encompassing shallow learning methods based on fixed molecular fingerprints, Graph Convolutional Neural Network (Graph-CNN) with its self-learned molecular representations, as well as ML methods based on combining fixed and Graph-CNN learned representations. RESULTS: Results of our ML models are compared both with respect to the aggregated predictive performance in terms of ROC-AUC based on the scaffold splits, as well as on the granular level of individual predictions, corresponding to the top ranked repurposing candidates. This comparison reveals both certain characteristic homogeneity regarding chemical and pharmacological classification, with a prevalence of sulfonamides and anticancer drugs, as well as identifies novel groups of potential drug candidates against COVID-19. CONCLUSIONS: A series of ML approaches for molecular property prediction enables drug discovery screenings, illustrating the utility for COVID-19. We show that the obtained results correspond well with the already published research on COVID-19 treatment, as well as provide novel insights on potential antiviral characteristics inferred from in vitro data. Twit Text FOAVip Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations ????J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=34052441 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34052441 #FOAvip English Wikipedia <ref>{{Cite pmid|34052441}}</ref> Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations #MMPMID34052441 Haneczok J; Delijewski M J Biomed Inform 2021[Jul]; 119 (ä): 103821 PMID34052441show ga AIM: Rapidly developing AI and machine learning (ML) technologies can expedite therapeutic development and in the time of current pandemic their merits are particularly in focus. The purpose of this study was to explore various ML approaches for molecular property prediction and illustrate their utility for identifying potential SARS-CoV-2 3CLpro inhibitors. MATERIALS AND METHODS: We perform a series of drug discovery screenings based on supervised ML models operating in different ways on molecular representations, encompassing shallow learning methods based on fixed molecular fingerprints, Graph Convolutional Neural Network (Graph-CNN) with its self-learned molecular representations, as well as ML methods based on combining fixed and Graph-CNN learned representations. RESULTS: Results of our ML models are compared both with respect to the aggregated predictive performance in terms of ROC-AUC based on the scaffold splits, as well as on the granular level of individual predictions, corresponding to the top ranked repurposing candidates. This comparison reveals both certain characteristic homogeneity regarding chemical and pharmacological classification, with a prevalence of sulfonamides and anticancer drugs, as well as identifies novel groups of potential drug candidates against COVID-19. CONCLUSIONS: A series of ML approaches for molecular property prediction enables drug discovery screenings, illustrating the utility for COVID-19. We show that the obtained results correspond well with the already published research on COVID-19 treatment, as well as provide novel insights on potential antiviral characteristics inferred from in vitro data. |*COVID-19 Drug Treatment[MESH] |*SARS-CoV-2[MESH] |Humans[MESH] |Machine Learning[MESH]Machine learning enabled identification of potential SARS-CoV-2 3CLpro(epmc).pdf DeepDyve Pubget Overpricing