ReviewCurrent drug discovery technologies2026
Quantum Computing in Drug Discovery Techniques, Challenges, and Emerging Opportunities.
Review in Current drug discovery technologies, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Opportunities and Risks of Technology Convergence in Precision Health.Blockchain in healthcare today · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionQuantum computing represents a transformative advancement in computational science, with applications in drug discovery, molecular interaction simulation, drug-target binding optimization, and the analysis of complex biological data at unprecedented speeds and accuracy. Quantum computing emerges as a powerful tool to accelerate the development of new therapeutics, drug design, and the simulation of complex chemical interactions, including personalized medicine strategies. The objective of this study is to explore the potential of quantum computing in drug discovery and development, highlighting its ability to reduce time and costs while accelerating the identification of promising drug candidates.
methodsQuantum computing algorithms, such as Grover's algorithm and the Variational Quantum Eigensolver (VQE), are utilized to simulate molecular interactions of drugs and optimize drug design. In case studies, such as IBM's use of VQE for molecular simulations, these technologies demonstrate their effectiveness.
resultsQuantum computing has shown promise in addressing several technological barriers, such as lengthy development timelines and high costs. Additionally, demonstrated success in molecular simulations and solving challenges during the drug development process. However, challenges related to error rates, qubit coherence, and regulatory compliance remain. DISCUSSION: This study examines the applications of quantum computing in drug discovery, highlighting key techniques such as quantum simulation, quantum machine learning, and optimization algorithms. Quantum computing is crucial for interdisciplinary collaboration among quantum physicists, computational chemists, biologists, and pharmaceutical professionals, as it is essential to overcoming these obstacles and realizing the full potential of quantum technologies in medicine.
conclusionQuantum computing holds great potential in drug discovery and development, offering accelerated, more accurate, and lower-cost research avenues, particularly in complex areas such as protein folding prediction and personalized medicine. This new paradigm has tremendous potential for guiding the future of pharmaceutical development and patient-focused medicine.
Indexed as
Identifiers
40873222What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.