Invited: Drug Discovery Approaches using Quantum Machine Learning

Junde Li; Mahabubul Alam; Congzhou M. Sha; Jian Wang; Nikolay V. Dokholyan; Swaroop Ghosh

doi:10.1109/DAC18074.2021.9586268

Invited: Drug Discovery Approaches using Quantum Machine Learning

Junde Li, Mahabubul Alam, Congzhou M. Sha, Jian Wang, Nikolay V. Dokholyan, Swaroop Ghosh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 Scopus citations

Abstract

Traditional drug discovery pipelines can require multiple years and billions of dollars of investment. Deep generative and discriminative models are widely adopted to assist in drug development. Classical machines cannot efficiently reproduce the atypical patterns of quantum computers, which may improve the quality of learned tasks. We propose a suite of quantum machine learning techniques: incorporating generative adversarial networks (GAN), convolutional neural networks (CNN) and variational auto-encoders (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively.

Original language	English (US)
Title of host publication	2021 58th ACM/IEEE Design Automation Conference, DAC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1356-1359
Number of pages	4
ISBN (Electronic)	9781665432740
DOIs	https://doi.org/10.1109/DAC18074.2021.9586268
State	Published - Dec 5 2021
Event	58th ACM/IEEE Design Automation Conference, DAC 2021 - San Francisco, United States Duration: Dec 5 2021 → Dec 9 2021

Publication series

Name	Proceedings - Design Automation Conference
Volume	2021-December
ISSN (Print)	0738-100X

Conference

Conference	58th ACM/IEEE Design Automation Conference, DAC 2021
Country/Territory	United States
City	San Francisco
Period	12/5/21 → 12/9/21

All Science Journal Classification (ASJC) codes

Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering
Modeling and Simulation

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10.1109/DAC18074.2021.9586268

Cite this

Li, J., Alam, M., Sha, C. M., Wang, J., Dokholyan, N. V., & Ghosh, S. (2021). Invited: Drug Discovery Approaches using Quantum Machine Learning. In 2021 58th ACM/IEEE Design Automation Conference, DAC 2021 (pp. 1356-1359). (Proceedings - Design Automation Conference; Vol. 2021-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DAC18074.2021.9586268

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title = "Invited: Drug Discovery Approaches using Quantum Machine Learning",

abstract = "Traditional drug discovery pipelines can require multiple years and billions of dollars of investment. Deep generative and discriminative models are widely adopted to assist in drug development. Classical machines cannot efficiently reproduce the atypical patterns of quantum computers, which may improve the quality of learned tasks. We propose a suite of quantum machine learning techniques: incorporating generative adversarial networks (GAN), convolutional neural networks (CNN) and variational auto-encoders (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively.",

author = "Junde Li and Mahabubul Alam and Sha, {Congzhou M.} and Jian Wang and Dokholyan, {Nikolay V.} and Swaroop Ghosh",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 58th ACM/IEEE Design Automation Conference, DAC 2021 ; Conference date: 05-12-2021 Through 09-12-2021",

year = "2021",

month = dec,

day = "5",

doi = "10.1109/DAC18074.2021.9586268",

language = "English (US)",

series = "Proceedings - Design Automation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2021 58th ACM/IEEE Design Automation Conference, DAC 2021",

address = "United States",

}

Li, J, Alam, M, Sha, CM, Wang, J , Dokholyan, NV & Ghosh, S 2021, Invited: Drug Discovery Approaches using Quantum Machine Learning. in 2021 58th ACM/IEEE Design Automation Conference, DAC 2021. Proceedings - Design Automation Conference, vol. 2021-December, Institute of Electrical and Electronics Engineers Inc., pp. 1356-1359, 58th ACM/IEEE Design Automation Conference, DAC 2021, San Francisco, United States, 12/5/21. https://doi.org/10.1109/DAC18074.2021.9586268

Invited: Drug Discovery Approaches using Quantum Machine Learning. / Li, Junde; Alam, Mahabubul; Sha, Congzhou M. et al.
2021 58th ACM/IEEE Design Automation Conference, DAC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1356-1359 (Proceedings - Design Automation Conference; Vol. 2021-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Li, Junde

AU - Alam, Mahabubul

AU - Sha, Congzhou M.

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AU - Ghosh, Swaroop

PY - 2021/12/5

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N2 - Traditional drug discovery pipelines can require multiple years and billions of dollars of investment. Deep generative and discriminative models are widely adopted to assist in drug development. Classical machines cannot efficiently reproduce the atypical patterns of quantum computers, which may improve the quality of learned tasks. We propose a suite of quantum machine learning techniques: incorporating generative adversarial networks (GAN), convolutional neural networks (CNN) and variational auto-encoders (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively.

AB - Traditional drug discovery pipelines can require multiple years and billions of dollars of investment. Deep generative and discriminative models are widely adopted to assist in drug development. Classical machines cannot efficiently reproduce the atypical patterns of quantum computers, which may improve the quality of learned tasks. We propose a suite of quantum machine learning techniques: incorporating generative adversarial networks (GAN), convolutional neural networks (CNN) and variational auto-encoders (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively.

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M3 - Conference contribution

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BT - 2021 58th ACM/IEEE Design Automation Conference, DAC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Invited: Drug Discovery Approaches using Quantum Machine Learning

Abstract

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