The recent pandemic outbreak has posed significant challenges for medical research, particularly in drug discovery. Machine learning (ML) has become increasingly prevalent in various stages of drug discovery, aiming to support the advancement of new drug research while reducing time and cost investments. Furthermore, the emergence of quantum computing and quantum machine learning (QML) represents a significant advancement in this field, offering the ability to tackle the complex processes involved in drug discovery. This review provides a comprehensive perspective, comparing advanced QML to classical ML in drug discovery applications including drug design, virtual screening, and absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction. Additionally, we summarize the current applications of QML algorithms to real-world datasets utilized in clinical research and drug discovery.
The recent pandemic outbreak has posed significant challenges for medical research, particularly in drug discovery. Machine learning (ML) has become increasingly prevalent in various stages of drug discovery, aiming to support the advancement of new drug research while reducing time and cost investments. Furthermore, the emergence of quantum computing and quantum machine learning (QML) represents a significant advancement in this field, offering the ability to tackle the complex processes involved in drug discovery. This review provides a comprehensive perspective, comparing advanced QML to classical ML in drug discovery applications including drug design, virtual screening, and absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction. Additionally, we summarize the current applications of QML algorithms to real-world datasets utilized in clinical research and drug discovery.
The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of ML, leading to the emergence of Quantum Machine Learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals against the classical ones and a hybrid solution is the recommended approach at present.
This article explores the transformative potential of Quantum Machine Learning (QML) in drug discovery. QML leverages quantum computing and advanced machine learning to accelerate the identification of drug candidates, predict molecular interactions, and optimize compounds. Key applications include efficient virtual screening, molecular simulation, and predictive modeling. While promising, QML faces technical challenges, requiring collaboration between quantum experts and pharmaceutical researchers. In summary, QML offers a faster, cost-effective path to drug development, with the potential to reshape the pharmaceutical industry and advance medical science.
Quantum computing plays a significant role in simulating molecules and atoms and offers advantages in chemistry over classical computing. The potential of Quantum Machine Learning (QML) can be used in drug discovery, chemical reaction simulations, and Material design for pharmaceuticals. QML leverages quantum computing and advanced machine learning to accelerate the identification of drug candidates, predict molecular interactions, and optimize compounds. In this paper, we present a systematic review of the methods used for molecular property prediction and molecular generation using quantum machine learning. We have included the recent research, perspective, advantages, and challenges that must be addressed to achieve this task. The objective of this research is not only to discuss current strategies and methods used for drug discovery but also to promote interdisciplinary research in the field of quantum computing and chemistry for wellness.
This article presents a compelling exploration into the transformative potential of Quantum Machine Learning (QML) in the realm of drug discovery. The main findings of this study underscore the profound impact of QML, as it offers a revolutionary approach to drug development. By synergizing quantum computing and machine learning, QML enables precision and efficiency at every stage of drug discovery. Its applications encompass target identification, molecular docking, pharmacokinetics prediction, toxicity assessment, and accelerated drug screening. The significance of these findings lies in the promise of expedited, cost-effective, and targeted drug development. The marriage of quantum computing and machine learning opens new frontiers for precision medicine and holds the potential to reshape the pharmaceutical landscape. This article delves into the foundational principles, real-world case studies, and ethical considerations surrounding the implementation of QML in drug discovery, illuminating its potential to revolutionize the field and improve patient outcomes.
Quantum machine learning (QML) has become a key research field on the interface between quantum computing, artificial intelligence, chemistry and pharmaceutical science. By enhancing the molecular representation, predictive modeling, generative chemistry, and quantum-assisted simulations workflows, researchers are increasingly arguing that QML can aid work in molecular design and drug discovery. Meanwhile, this field is still in its new frontiers with much of the published literature only comprising reviews, simulations, proof-of-concept applications, and hybrid quantum-classical demonstrations as opposed to large-scale pharmaceutical application. In this qualitative study, the literature emerging is reviewed to determine how QML is being conceptualized, areas of application seem the most promising, and what obstacles remain that hamper its practical implementation in actual drug discovery pipelines. The paper presents five large themes segmented into a thematic analysis of recent reviews and representative studies and includes relevant themes as QML as a representational innovation, QML in predictive tasks, e.g., drug-target interaction and ADMET modeling, QML in generative molecular design, hybrid quantum-classical workflows as the most plausible approach in the near term, and the continuation of restrictions in terms of hardware, trainability, data This research paper concludes that the idea of QML must be seen at present as a field not as an alternative to the classical machine learning in drug discovery, but it is an opportunity with strategic and scientific potential whose future realization may be found in the subproblems carefully chosen where the influence of molecular quantum effects, the complexity of learning, and the hybrid implementations converge. Keywords: quantum machine learning, molecular design, drug discovery, qualitative study, quantum computing, generative chemistry, ADMET, drug-target interaction
At the forefront of innovation in drug discovery lies quantum machine learning (QML), poised to revolutionize the field with its unparalleled potential. Through the amalgamation of quantum computing and machine learning principles, QML presents novel solutions to complex computational challenges inherent in pharmaceutical research. The chapter explores the multifaceted applications of QML in drug discovery, including predictive modeling of pharmacokinetic and pharmacodynamic properties, personalized medicine, and precision drug design. Despite facing challenges, such as data quality and algorithm optimization, the future of QML in drug discovery is promising. As researchers continue to explore and refine QML techniques, they pave the way for enhanced therapeutic efficacy, optimized combination therapies, and tailored treatment strategies, ultimately advancing the field of drug discovery into new frontiers.