Drug Discovery Approaches using Quantum Machine Learning

• URL: http://arxiv.org/abs/2104.00746v1
• Date: Thu, 1 Apr 2021 19:53:06 GMT
• Title: Drug Discovery Approaches using Quantum Machine Learning
• Authors: Junde Li, Mahabubul Alam, Congzhou M Sha, Jian Wang, Nikolay V. Dokholyan, Swaroop Ghosh
• Abstract summary: Deep generative and predictive models are widely adopted to assist in drug development. We propose a suite of quantum machine learning techniques to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules.
• Score: 10.321495133438242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional drug discovery pipeline takes several years and cost billions of dollars. Deep generative and predictive models are widely adopted to assist in drug development. Classical machines cannot efficiently produce atypical patterns of quantum computers which might improve the training quality of learning tasks. We propose a suite of quantum machine learning techniques e.g., generative adversarial network (GAN), convolutional neural network (CNN) and variational auto-encoder (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively.

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