Artificial intelligence (AI) has greatly expanded the generative capacity of drug discovery, yet the ability to translate large candidate pools into structured, resource-aware decisions remains limited. This review addresses this emerging optimization bottleneck by examining quantum annealing as a potential decision-optimization layer within hybrid computational workflows. We focus on discrete, constraint-dominated tasks-such as compound subset selection, combinatorial design, and multi-objective prioritization-that can be formulated as quadratic unconstrained binary optimization (QUBO) problems. Rather than positioning quantum annealing as a predictive tool, we analyze its role as a complementary optimization interface integrated with AI-generated scores. We further discuss practical implementation considerations, including embedding overhead, noise, scalability, and benchmarking challenges. By emphasizing workflow-level design and rigorous evaluation, this review provides a pragmatic framework for assessing annealing-based optimization in drug discovery and related data-intensive scientific domains.
Artificial intelligence (AI) has greatly expanded the generative capacity of drug discovery, yet the ability to translate large candidate pools into structured, resource-aware decisions remains limited. This review addresses this emerging optimization bottleneck by examining quantum annealing as a potential decision-optimization layer within hybrid computational workflows. We focus on discrete, constraint-dominated tasks-such as compound subset selection, combinatorial design, and multi-objective prioritization-that can be formulated as quadratic unconstrained binary optimization (QUBO) problems. Rather than positioning quantum annealing as a predictive tool, we analyze its role as a complementary optimization interface integrated with AI-generated scores. We further discuss practical implementation considerations, including embedding overhead, noise, scalability, and benchmarking challenges. By emphasizing workflow-level design and rigorous evaluation, this review provides a pragmatic framework for assessing annealing-based optimization in drug discovery and related data-intensive scientific domains.
Abstract : We propose a hybrid quantum–classical framework that integrates variational quantum machine learning (QML) for molecular binding affinity prediction with quantum annealing for Pareto-optimal drug formulation design. Leveraging a four-qubit variational circuit, our QML model achieves superior predictive performance compared to state-of-the-art classical baselines, effectively capturing complex molecular interactions. In parallel, we formulate drug formulation optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solve it using D-Wave’s quantum annealer, enabling efficient identification of Pareto-optimal solutions that balance bioavailability, toxicity, and stability. By unifying predictive modeling and combinatorial optimization within a single pipeline, our framework demonstrates the potential of near-term quantum devices to accelerate and enhance critical stages of drug discovery. These findings underscore the transformative role of NISQ-era algorithms in bridging molecular informatics and formulation design, paving the way for scalable, data-driven pharmaceutical innovation.
Combinatorial optimization is a foundation of numerous scientific, industrial, and social decision-making questions, but its usefulness is mostly restricted by the exponential complexity of the problem of classical computability. The quantum annealing (QA) has become one of the leading analogue quantum systems to address such problems, whereby optimization problems are converted into physical energy structures and quantum fluctuations are utilized to perform systematic exploration. It provides a critical, synthesized, and comprehensive review of quantum annealing as applied to combinatorial optimization, including the theoretical basis, hardware designs, algorithm methods, issues of embedding and encoding, benchmarking procedures, applications, and integration with quantum algorithms based on gates and solvers on a classical computer. We build a comprehensive taxonomy between adiabatic dynamics, Ising and QUBO models, stoquastic and non-stoquastic Hamiltonians, and diabatic transitions to state-of-the-art flux-qubit annealers, new architectures, and hybrid quantum classical pipelines. We show that the overhead of embedding and encoding and the benchmarking approach taken are the most significant factors in determining scalability and performance, but not the raw number of qubits. Transportation, energy systems, robotics, finance, pharmacological discovery, and machine learning are just some examples of the domain-specific case studies that use QA with minimal empirical utility as a hybrid refinement engine and not an independent solver. Moreover, we introduce a stringent evaluation of the existing benchmarking culture, clarify the causal links between QA and QAOA as well as between VQE and structural predicaments, and reveal the institutional roots of the existing restrictions. We conclude by identifying a research roadmap in the future, focusing on the annealing hardness characterization, Stoquastic control manipulation, embedding rules automation, architecture design, and development of principled quantum advantage. It creates a benchmark reference to those in the field and research and deployment of scalable, reliable, and application-relevant quantum optimization
The integration of Artificial intelligence (AI) and Quantum computing (QC) is redefining the future of pharmaceutical research and development.While AI-particularly Machine learning (ML) and deep learning-accelerates 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 quantum-classical 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 Structure-Activity Relationship (QSAR) models, provide limited guidance for validating complex AI-QC 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 AI-QC 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 globally-but its promise can only be realized through proactive, transparent, and ethical governance.
We propose a unified approach for reformulating diverse computational challenges into Quadratic Unconstrained Binary Optimization (QUBO) models and solving them via quantum annealing. By applying this framework across multiple domains, we demonstrate its flexibility in handling structurally different problems within a single mathematical formulation. The study highlights QUBO’s capability to exploit quantum parallelism, integrate regularization seamlessly, and address ill-posed inverse problems. These advantages position QUBO-based quantum annealing as a promising tool for large-scale optimization and reconstruction tasks, with strong potential for impact in scientific computing, engineering, and data-driven applications.
The systematic discovery of effective drug combinations is a challenging problem in modern pharmacology, driven by the combinatorial growth of potential pairings and dosage configurations. Network medicine, modeling diseases and drugs as interconnected modules of the human protein-protein interactome, has emerged as a new paradigm for understanding disease mechanisms and drug action. In this work, we propose a quantum annealing-based algorithm for identifying effective drug combinations. Underlying our approach is the biologically motivated principle of `Complementary Exposure', which posits that therapeutic drug combinations target distinct yet complementary regions of a disease module. We translate this into a quadratic unconstrained binary optimisation problem. We test our method for Diabetes Mellitus, Rheumatoid Arthritis, Asthma, and Brain Neoplasms, relying on experimentally validated drug combinations for these diseases. Our simulated quantum annealing experiments reveal that low-energy configurations align with biologically plausible combinations which may serve as prioritized candidates for subsequent experimental validation.
Over the past few years, artificial intelligence (AI) and quantum computing have emerged as groundbreaking technologies in the field of drug discovery. Techniques like AI-driven molecular design, quantum-enhanced simulations, and hybrid approaches have made significant strides in tackling the complexity of drug-target interactions. However, current models still face key challenges, such as the limited scalability of quantum systems, the difficulty of generalizing AI models due to insufficient data quality, and the inefficiencies in merging quantum and classical methods. To overcome these obstacles, this paper introduces a new hybrid quantum-AI workflow. By combining the precision of quantum computing for molecular simulations with the predictive power of AI, this approach aims to improve accuracy, scalability, and efficiency in drug discovery, paving the way for future breakthroughs in the field.