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10.2174/0929867328666210504114351 http://scihub22266oqcxt.onion/10.2174/0929867328666210504114351 33949929!ä!33949929 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 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       Curr+Med+Chem 2021 ; 28 (38): 7840-7861 Nephropedia Template TP {{tp|p=33949929|t=2021. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy |pdf=|usr=}}{{33949929}} gab.com Text Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy JO: Curr Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=33949929 #FOAvip Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others lead to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction and analysis of virus-host interactions. Our review also covers the perspectives of using the machine learning approaches for antiviral research including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses and some others. Twit Text FOAVip Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy ????Curr Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=33949929 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33949929 #FOAvip English Wikipedia <ref>{{Cite pmid|33949929}}</ref> Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy #MMPMID33949929 Tarasova O; Poroikov V Curr Med Chem 2021[]; 28 (38): 7840-7861 PMID33949929show ga Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others lead to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction and analysis of virus-host interactions. Our review also covers the perspectives of using the machine learning approaches for antiviral research including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses and some others. |*HIV Infections/drug therapy[MESH] |*Influenza, Human/drug therapy[MESH] |*Virus Diseases/drug therapy[MESH] |Antiviral Agents/pharmacology/therapeutic use[MESH] |Humans[MESH]Machine Learning in Discovery of New Antivirals and Optimization of Vi(epmc).pdf DeepDyve Pubget Overpricing