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.
@article{arxiv.2104.00746,
title = {Drug Discovery Approaches using Quantum Machine Learning},
author = {Junde Li and Mahabubul Alam and Congzhou M Sha and Jian Wang and Nikolay V. Dokholyan and Swaroop Ghosh},
journal= {arXiv preprint arXiv:2104.00746},
year = {2021}
}
Comments
Li and Alam contributed equally to this work. arXiv admin note: text overlap with arXiv:2101.03438