The pharmaceutical industry faces big challenges in drug discovery today – rising costs, high failure rates, and increasingly complex target diseases. Regular computational methods often fail when trying to model biological systems, predict how molecules behave, or search through massive chemical space. Quantum computing might completely change how we solve these problems. As quantum technology and its programming improve, we are approaching a significant transformation in computational drug discovery.

This Research Topic aims to explore the progress of quantum computing, emerging applications, and future potential in revolutionizing drug discovery processes. We seek contributions that connect quantum algorithm theory with real-world pharmaceutical applications, highlighting both immediate opportunities on current noisy intermediate-scale quantum (NISQ) devices and long-term possibilities offered by fault-tolerant quantum computers. Drug discovery faces some basic computing problems: the massive computing power needed to accurately model molecules, the overwhelming number of chemical combinations to explore, and the difficulty of predicting how proteins and drug candidates interact. Quantum computing's unique properties, like superposition and entanglement, could provide more effective solutions to these issues.
Nevertheless, significant challenges still remain today, e.g., quantum computers are limited by noise and qubit counts, optimal encodings of chemical problems onto quantum registers are still being developed, and the field requires interdisciplinary expertise from quantum physics, computer science, and pharmaceutical research. This Research Topic aims to address these issues by bringing different viewpoints and methods together.

Contributions may address (but are not limited to) the following areas:
• Algorithmic Developments: Quantum machine learning for molecular property prediction, variational quantum algorithms for simulations, quantum optimization for molecular docking
• Hardware-Specific Applications: NISQ-era implementations, quantum-classical hybrid approaches, error mitigation techniques for pharmaceutical calculations
• Real-World Case Studies: Protein folding simulations, fragment-based drug discovery, reaction mechanism elucidation
• Forward-Looking Research: Fault-tolerant quantum algorithms for drug discovery, quantum approaches to ADMET prediction, quantum-enhanced molecular dynamics, exploration of chemical space

This Research Topic aims to be a key collection showing both current advances and future directions in this fast-changing field. By bringing researchers from academia and industry from quantum computing and pharmaceutical sciences, we hope to speed up progress toward practical quantum advantages in drug discovery.
We particularly encourage submissions that bridge theoretical quantum computing with practical pharmaceutical needs, and those that clearly show the advantage of quantum approaches over classical computing methods.

We welcome various contributions, including:
• Original Research Articles: Presenting novel quantum algorithms, applications, or experimental results relevant to drug discovery challenges
• Methods Papers: Describing new methodological approaches for implementing drug discovery workflows on quantum computers
• Review Articles: Synthesizing the state-of-the-art in specific applications of quantum computing to drug discovery
• Perspective Pieces: Offering expert views on the future direction and potential impact of quantum computing in pharmaceutical research
• Case Studies: Demonstrating practical implementations or proof-of-concept applications, even on simulated quantum systems.

Are you interested in contributing a manuscript to this Research Topic?

Emerging Technologies: Quantum Computing for Drug Discovery