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October 12, 2025Open Access

Quantum Machine Learning for Drug Discovery: From Molecular Descriptors to Explainable Quantum Pharmacology

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Authors

VEVolkan ErolMarmara University

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Overview

Hybrid approach improves ligand-target binding predictions in drug discovery, highlighting quantum pharmacology's implications.

Key Points

  • Quantum embeddings in drug discovery achieve competitive predictive accuracy and improved stability.
  • A synthetic dataset reflecting BindingDB was used to evaluate the performance of quantum descriptors.
  • Comparative analyses with classical methods revealed quantum models’ potential in predicting ligand-target binding.
  • Explainable Quantum Pharmacology emphasizes the importance of interpretability alongside accuracy in drug discovery.

Cite This Study

Volkan Erol (2025) studied this question.

synapsesocial.com/papers/68ebffcfdef9fcb308ff252dhttps://doi.org/10.20944/preprints202510.0790.v1
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  1. 1Quantum Machine Learning for Drug Discovery: A Systematic Review2025
  2. 2Variational Quantum Regression for Binding Affinity Prediction: A Hybrid Quantum-Classical Framework with Explainable Molecular Descriptors2025 · 1 citations
  3. 3Quantum Long Short-Term Memory for Drug Discovery2024 · 1 citations
  4. 4Quantum–classical hybrid learning framework for molecular property prediction and molecule optimization in drug discovery2026
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