Hybrid Quantum-Classical Machine Learning for Drug Discovery

EasyChair Preprint 14327

Abstract

The rapid advancement of quantum computing presents unprecedented opportunities for drug discovery by enhancing the capabilities of traditional computational methods. This research explores the integration of hybrid quantum-classical machine learning techniques to accelerate the identification and optimization of potential drug candidates. By leveraging quantum computing for complex molecular simulations and combining it with classical machine learning algorithms for data analysis and pattern recognition, we aim to overcome the limitations of current drug discovery processes. The study focuses on developing hybrid models that can efficiently handle the vast chemical space, predict molecular properties with high accuracy, and identify promising drug candidates. Key applications include the optimization of molecular structures, prediction of binding affinities, and simulation of drug-receptor interactions. This interdisciplinary approach not only enhances the efficiency and accuracy of drug discovery but also provides deeper insights into the molecular mechanisms underlying diseases. The findings from this research highlight the transformative potential of hybrid quantum-classical machine learning in revolutionizing pharmaceutical research and development, paving the way for the discovery of novel therapeutics.

Keyphrases: Hybrid Quantum-Classical Machine learning, binding affinity prediction, data analysis, drug discovery, drug-receptor interactions, molecular optimization, molecular simulations, novel therapeutics, pharmaceutical research, quantum computing

@booklet{EasyChair:14327,
  author    = {Hubert Klaus and Dylan Stilinki},
  title     = {Hybrid Quantum-Classical Machine Learning for Drug Discovery},
  howpublished = {EasyChair Preprint 14327},
  year      = {EasyChair, 2024}}