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10.1021/acs.jcim.1c00166 http://scihub22266oqcxt.onion/10.1021/acs.jcim.1c00166 34032436!8254374!34032436     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 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\34032436.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Chem+Inf+Model 2021 ; 61 (6): 2641-2647 Nephropedia Template TP {{tp|p=34032436|t=2021. Quantum Machine Learning Algorithms for Drug Discovery Applications |pdf=|usr=}}{{34032436}} gab.com Text Quantum Machine Learning Algorithms for Drug Discovery Applications JO: J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=34032436 #FOAvip The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field. Twit Text FOAVip Quantum Machine Learning Algorithms for Drug Discovery Applications ????J Chem Inf Model http://www.kidney.de/pget.php?&tw=1&pmid=34032436 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34032436 #FOAvip English Wikipedia <ref>{{Cite pmid|34032436}}</ref> Quantum Machine Learning Algorithms for Drug Discovery Applications #MMPMID34032436 Batra K; Zorn KM; Foil DH; Minerali E; Gawriljuk VO; Lane TR; Ekins S J Chem Inf Model 2021[Jun]; 61 (6): 2641-2647 PMID34032436show ga The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field. |*COVID-19[MESH] |*Drug Discovery[MESH] |Algorithms[MESH] |Computing Methodologies[MESH] |Humans[MESH] |Machine Learning[MESH] |Quantum Theory[MESH] |SARS-CoV-2[MESH]Quantum Machine Learning Algorithms for Drug Discovery Applications (epmc).pdf DeepDyve Pubget Overpricing