Paper Contents
Abstract
The integration of Artificial intelligence (AI) and Quantum computing (QC) is redefining the future of pharmaceutical research and development. While AIparticularly Machine learning (ML) and deep learningaccelerates target identification, virtual screening, and de novo drug design, it often suffers from interpretability issues, data bias, and limited mechanistic understanding. Conversely, QC leverages quantum mechanics to achieve unprecedented accuracy in molecular simulations, yet remains restricted by noise, limited qubits in the Noisy Intermediate-Scale Quantum (NISQ) era, and high costs. Emerging hybrid quantumclassical algorithms and Quantum Machine Learning (QML) approaches bridge these gaps, enabling high-fidelity in silico drug discovery workflows.Despite these advances, the regulatory environment has not kept pace. Current frameworks, such as Physiologically Based Pharmacokinetic (PBPK) modelling and Quantitative StructureActivity Relationship (QSAR) models, provide limited guidance for validating complex AIQC systems. Challenges include the lack of explainable artificial intelligence (XAI), uncertainty in verifying quantum outputs, data integrity, and accountability in hybrid pipelines. These gaps highlight the urgent need for regulatory science to adopt dynamic validation frameworks, algorithmic audits, and international harmonization.This review critically examines the technological and regulatory dimensions of AIQC synergy in drug discovery, outlining key applications, limitations, and ethical considerations. We propose pathways for explainability-by-design, continuous learning oversight, and regulatory sandboxes to facilitate safe and effective adoption. The synergy of AI and QC offers the potential to accelerate timelines, reduce costs, and deliver precision medicines globallybut its promise can only be realized through proactive, transparent, and ethical governance.
Copyright
Copyright © 2025 Gayatree K. Mohanty. This is an open access article distributed under the Creative Commons Attribution License.
