A hybrid quantum computing pipeline for real world drug discovery

Area of Science:

• Computational chemistry
• Quantum computing applications
• Pharmaceutical sciences

Background:

• Quantum computing offers superior computational power for scientific discovery.
• Current quantum computing applications in drug discovery are often limited to theoretical proof-of-concept studies.
• Real-world drug development faces complex challenges not fully addressed by existing models.

Purpose of the Study:

• To develop a hybrid quantum computing pipeline for genuine drug design problems.
• To address critical tasks such as Gibbs free energy profile determination and covalent bond simulation.
• To transition quantum computing from theoretical models to tangible applications in drug discovery.

Main Methods:

• Development of a versatile, hybrid quantum computing pipeline.
• Application of quantum computation for precise Gibbs free energy profile determination in prodrug activation.
• Accurate simulation of covalent bond interactions using quantum methods.

Main Results:

• The pipeline successfully addressed critical drug design tasks, including covalent bond cleavage and interactions.
• Benchmarking demonstrated the potential of quantum computing for realistic drug design scenarios.
• The study validates the integration of quantum computing into drug design workflows.

Conclusions:

• The developed hybrid quantum computing pipeline is suitable for real-world drug design challenges.
• Quantum computing can be effectively applied to simulate complex chemical interactions like covalent bonds.
• This work paves the way for scalable quantum computation in pharmaceutical research and development.