# Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage

arXiv: https://arxiv.org/abs/2204.04017 · HTML: https://mlsanity.com/paper/2204.04017/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [A rigorous and robust quantum speed-up in supervised machine learning](https://mlsanity.com/paper/2010.02174/) (2020)
- [Autonomous discovery in the chemical sciences part II: Outlook](https://mlsanity.com/paper/2003.13755/) (2020)
- [Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization](https://mlsanity.com/paper/1706.06619/) (2017)

## Built on by (most cited first, 18 of 18)

- [Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications](https://mlsanity.com/paper/2307.00908/) (2023)
- [Hyperparameter Importance of Quantum Neural Networks Across Small Datasets](https://mlsanity.com/paper/2206.09992/) (2022)
- [A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms](https://mlsanity.com/paper/2208.13251/) (2022)
- [Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators](https://mlsanity.com/paper/2505.17756/) (2025)
- [Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training](https://mlsanity.com/paper/2401.02879/) (2024)
- [A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery](https://mlsanity.com/paper/2309.03919/) (2023)
- [Multi-Class Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2404.12741/) (2024)
- [Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles](https://mlsanity.com/paper/2505.08782/) (2025)
- [Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy](https://mlsanity.com/paper/2508.00768/) (2025)
- [Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case](https://mlsanity.com/paper/2402.15542/) (2024)
- [Hybrid Quantum Graph Neural Network for Molecular Property Prediction](https://mlsanity.com/paper/2405.05205/) (2024)
- [Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunities](https://mlsanity.com/paper/2412.12373/) (2024)
- [Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning](https://mlsanity.com/paper/2602.00048/) (2026)
- [Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex](https://mlsanity.com/paper/2306.07484/) (2023)
- [Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region](https://mlsanity.com/paper/2411.18141/) (2024)
- [Quantum Machine Learning in Precision Medicine and Drug Discovery -- A Game Changer for Tailored Treatments?](https://mlsanity.com/paper/2502.18639/) (2025)
- [Quantum feature encoding optimization](https://mlsanity.com/paper/2512.02422/) (2025)
- [Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks](https://mlsanity.com/paper/2609.05060/) (2026)

## Abstract (arXiv)

Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier (SVC) algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.