# Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries

arXiv: https://arxiv.org/abs/2409.15645 · HTML: https://mlsanity.com/paper/2409.15645/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 100 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [Mol-CycleGAN - a generative model for molecular optimization](https://mlsanity.com/paper/1902.02119/) (2019)
- [Quantum Vision Transformers](https://mlsanity.com/paper/2209.08167/) (2022)
- [Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction](https://mlsanity.com/paper/2109.12636/) (2021)
- [Quantum autoencoders via quantum adders with genetic algorithms](https://mlsanity.com/paper/1709.07409/) (2017)
- [Experimental Implementation of a Quantum Autoencoder via Quantum Adders](https://mlsanity.com/paper/1807.10643/) (2018)
- [DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis](https://mlsanity.com/paper/2405.13983/) (2024)
- [Quantum activation functions for quantum neural networks](https://mlsanity.com/paper/2201.03700/) (2022)
- [ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation](https://mlsanity.com/paper/2309.05853/) (2023)
- [HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2212.12440/) (2022)
- [Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning](https://mlsanity.com/paper/2305.18961/) (2023)
- [Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applications](https://mlsanity.com/paper/2101.04869/) (2021)
- [Prime Factorization Using Quantum Variational Imaginary Time Evolution](https://mlsanity.com/paper/2107.10196/) (2021)
- [Kernel-Elastic Autoencoder for Molecular Design](https://mlsanity.com/paper/2310.08685/) (2023)

## Built on by (most cited first, 3 of 3)

- [Layerwise Federated Learning for Heterogeneous Quantum Clients using Quorus](https://mlsanity.com/paper/2510.06228/) (2025)
- [Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning](https://mlsanity.com/paper/2602.00048/) (2026)
- [An Efficient Quantum Circuit for Flow Model Execution Using Quantum Neural Networks](https://mlsanity.com/paper/2610.11537/) (2026)

## Abstract (arXiv)

The nexus of quantum computing and machine learning - quantum machine learning - offers the potential for significant advancements in chemistry. This review specifically explores the potential of quantum neural networks on gate-based quantum computers within the context of drug discovery. We discuss the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. Applications to drug discovery are highlighted, including molecular property prediction and molecular generation. We provide a balanced perspective, emphasizing both the potential benefits and the challenges that must be addressed.