mlsanityData to 7 Oct 2026

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

arXiv 2204.04017 ↗First posted 8 Apr 2022quant-ph cs.LG physics.chem-phcited by 18

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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.

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

Builds on

7 of 55 references.

References · 7

  1. 2018Supervised learning with quantum enhanced feature spacescited by 654
  2. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  3. 2020Power of data in quantum machine learningcited by 336
  4. 2016Quantum Machine Learningcited by 325
  5. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 270
  6. 2020Autonomous discovery in the chemical sciences part II: Outlookcited by 26
  7. 2017Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalizationcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 6; 2025: 5; 2026: 2.

18 papers.

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  1. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  2. 2022Hyperparameter Importance of Quantum Neural Networks Across Small Datasetscited by 7
  3. 2022A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithmscited by 7
  4. 2025Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulatorscited by 7
  5. 2024Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Trainingcited by 6
  6. 2023A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discoverycited by 4
  7. 2024Multi-Class Quantum Convolutional Neural Networkscited by 3
  8. 2025Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensemblescited by 3
  9. 2024Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Casecited by 1
  10. 2024Hybrid Quantum Graph Neural Network for Molecular Property Predictioncited by 1
  11. 2025Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracycited by 1
  12. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
6 more
  1. 2023Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complexcited by 0
  2. 2024Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Regioncited by 0
  3. 2024Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunitiescited by 0
  4. 2025Quantum Machine Learning in Precision Medicine and Drug Discovery -- A Game Changer for Tailored Treatments?cited by 0
  5. 2025Quantum feature encoding optimizationcited by 0
  6. 2026Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networkscited by 0