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Quantum Machine Learning Algorithms for Drug Discovery Applications.
Batra, Kushal; Zorn, Kimberley M; Foil, Daniel H; Minerali, Eni; Gawriljuk, Victor O; Lane, Thomas R; Ekins, Sean.
  • Batra K; Computer Science, North Carolina State University, Raleigh, North Carolina 27606, United States.
  • Zorn KM; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Foil DH; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Minerali E; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Gawriljuk VO; São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100 - Santa Angelina, São Carlos - São Paulo 13563-120, Brazil.
  • Lane TR; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Ekins S; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
J Chem Inf Model ; 61(6): 2641-2647, 2021 06 28.
Article in English | MEDLINE | ID: covidwho-1241784
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Discovery / COVID-19 Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2021 Document Type: Article Affiliation country: Acs.jcim.1c00166

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Discovery / COVID-19 Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2021 Document Type: Article Affiliation country: Acs.jcim.1c00166