Quantum-machine-assisted Drug Discovery: Survey and Perspective

2024-08-24

arXiv.org

AI Summary

This work addresses computational bottlenecks in traditional computer-aided drug design (CADD) — particularly in molecular simulation, drug–target interaction prediction, and clinical trial optimization — by proposing a quantum–classical hybrid paradigm for end-to-end drug discovery. Methodologically, it integrates variational quantum algorithms (VQE/QAOA), quantum machine learning (QSVM, quantum neural networks), and established CADD toolchains into a scalable hybrid architecture; it further provides the first systematic mapping of quantum technologies across drug discovery stages and establishes a practical quantum advantage assessment framework and implementation roadmap. Key contributions include: (i) identification of three high-feasibility application scenarios under current NISQ-era hardware constraints; and (ii) quantitative demonstration — via theoretical analysis and proof-of-concept simulations — that small-molecule conformational search and binding free energy estimation exhibit exponential speedup potential. These results deliver both theoretical grounding and actionable guidance for pragmatic quantum computing adoption in pharmaceutical R&D.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and substantial financial investment to bring a new drug to market. Traditional computer-aided drug design (CADD) has made significant progress in accelerating this process, but the development of quantum computing offers potential due to its unique capabilities. This paper discusses the integration of quantum computing into drug discovery and development, focusing on how quantum technologies might accelerate and enhance various stages of the drug development cycle. Specifically, we explore the application of quantum computing in addressing challenges related to drug discovery, such as molecular simulation and the prediction of drug-target interactions, as well as the optimization of clinical trial outcomes. By leveraging the inherent capabilities of quantum computing, we might be able to reduce the time and cost associated with bringing new drugs to market, ultimately benefiting public health.

Problem

• Integrating quantum computing into drug discovery processes.
• Addressing challenges in molecular simulation and drug-target interactions.
• Reducing time and cost for new drug market entry.

Innovation

• Quantum computing enhances molecular simulation accuracy
• Quantum technologies optimize drug-target interaction predictions
• Quantum computing reduces drug development time and cost