Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery

Junde Li, Swaroop Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

The de novo design of drug molecules is recognized as a time-consuming and costly process, and computational approaches have been applied in each stage of the drug discovery pipeline. Variational autoencoder is one of the computer-aided design methods which explores the chemical space based on an existing molecular dataset. Quantum machine learning has emerged as an atypical learning method that may speed up some classical learning tasks because of its strong expressive power. However, near-term quantum computers suffer from limited num-ber of qubits which hinders the representation learning in high dimensional spaces. We present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction. The architectural strategies in hybrid quantum classical networks such as, adjustable quantum layer depth, heterogeneous learning rates, and patched quantum circuits are proposed to learn high dimensional dataset such as, ligand-targeted drugs. Extensive experimental results are reported for different dimensions including 8x8 and 32x32 after choosing suitable architectural strategies. The performance of quantum generative autoencoder is compared with the corre-sponding classical counterpart throughout all experiments. The results show that quantum computing advantages can be achieved for normalized low-dimension molecules, and that high-dimension molecules generated from quantum generative autoencoders have better drug properties within the same learning period.

Original languageEnglish (US)
Title of host publicationProceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022
EditorsCristiana Bolchini, Ingrid Verbauwhede, Ioana Vatajelu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages340-345
Number of pages6
ISBN (Electronic)9783981926361
DOIs
StatePublished - 2022
Event2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022 - Virtual, Online, Belgium
Duration: Mar 14 2022Mar 23 2022

Publication series

NameProceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022

Conference

Conference2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022
Country/TerritoryBelgium
CityVirtual, Online
Period3/14/223/23/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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