# Drug Discovery Approaches using Quantum Machine Learning

arXiv: https://arxiv.org/abs/2104.00746 · HTML: https://mlsanity.com/paper/2104.00746/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [Experimental Quantum Generative Adversarial Networks for Image Generation](https://mlsanity.com/paper/2010.06201/) (2020)
- [Quantum Generative Models for Small Molecule Drug Discovery](https://mlsanity.com/paper/2101.03438/) (2021)

## Built on by (most cited first, 4 of 4)

- [Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)](https://mlsanity.com/paper/2109.02862/) (2021)
- [Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery](https://mlsanity.com/paper/2112.12563/) (2021)
- [A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning](https://mlsanity.com/paper/2305.10212/) (2023)
- [Differentiable Rotamer Sampling with Molecular Force Fields](https://mlsanity.com/paper/2302.11430/) (2023)

## Abstract (arXiv)

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.