Quantum Artificial Intelligence for Protein Structure Prediction

Arunkumar Selvaraj, Maharsh Jayawant, Nagarajan Kayalvizhi +4 more

Troy Coienth Troublefield

The convergence of CRISPR gene-editing technology with quantum computing and artificial intelligence represents a paradigm shift in biomedical innovation. This interdisciplinary synthesis addresses current limitations in genomic analysis, off-target prediction, protein folding simulations, and gene therapy design through unprecedented computational power and modeling sophistication. As CRISPR transforms our ability to manipulate genetic material with precision, quantum AI offers solutions to overcome bottlenecks in speed, accuracy, and complexity management. This article explores the emerging synergies between these technologies, examining their potential to unlock breakthroughs in personalized medicine, disease eradication, biosecurity, and synthetic biology. Through comprehensive analysis of current capabilities, implementation challenges, and future directions, we present a roadmap for responsible innovation at the intersection of quantum computing, artificial intelligence, and genome editing.

Shakila Basheer

By facilitating quicker and more precise analysis of intricate biological data, the union of quantum computing and Artificial Intelligence (AI) may transform genomic medicine and drug discovery altogether. The study suggests a model for artificial intelligence with quantum-enhanced acceleration based on hybrid quantum-classical models in order to speed up drug discovery and customize therapies. It utilizes deep learning models and Quantum Machine Learning (QML) techniques to analyze patient-specific data, protein structures, and genetic sequences. Large genomic and clinical data sets are processed by conventional deep learning models, while the quantum component carries out efficient data encoding and optimization processes. As compared to other traditional AI-based approaches, the proposed model performs a 30% improved drug target identification when evaluated against more than 1,000 genetic sequences and related clinical outcomes. The hybrid model was as accurate as 98.4% in its prediction and was more accurate than the conventional AI techniques including random forests (accuracy: 91.3%) and gradient boosting machines (accuracy: 93.7%). The methodology also produced an F1-score of 0.95 with accuracy and recall scores of 96.1% and 94.7%, respectively. These results demonstrate how quantum-enhanced AI can reduce the time for computation and improve the accuracy of genomic data analysis, driving significant progress toward personalized therapy and drug development.

Paridhi Chawhan, Ishita Singh

Renata Wong, Weng-Long Chang

Robert Skopec

Artificial Intelligence (AI) research has a lot to learn from nature. My work links biology with computation and AI every day, but recently the rest of the world was reminded of the connection: The 2018 Nobel Prize in Chemistry went to Frances Arnold together with George Smith and Gregory Winter for developing major breakthroughs that are collectively called “directed evolution.” One of its uses is to improve protein functions, making them better catalysts in biofuel production. Another use is entirely outside chemistry – outside even the traditional life sciences. It’s about my main innovation that Evolution continues with Artificial Intelligence and at the field of Quantum Biology, through the Economics, until the field of Military Self-Defense.