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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Quantum Machine Learning Algorithms for Drug Discovery Applications

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Batra, Kushal [1] ; Zorn, Kimberley M. [2] ; Foil, Daniel H. [2] ; Minerali, Eni [2] ; Gawriljuk, Victor O. [3] ; Lane, Thomas R. [2] ; Ekins, Sean [2]
Total Authors: 7
[1] North Carolina State Univ, Comp Sci, Raleigh, NC 27606 - USA
[2] Collaborat Pharmaceut Inc, Raleigh, NC 27606 - USA
[3] Univ Sao Paulo, Sao Carlos Inst Phys, BR-13563120 Sao Carlos, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF CHEMICAL INFORMATION AND MODELING; v. 61, n. 6, p. 2641-2647, JUN 28 2021.
Web of Science Citations: 0

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. (AU)

FAPESP's process: 19/25407-2 - Development of machine learning models for the discovery of new antiviral compounds against yellow fever virus
Grantee:Victor Gawriljuk Ferraro Oliveira
Support type: Scholarships abroad - Research Internship - Master's degree