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 machine learning (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 to the classical ones, and a hybrid solution is the recommended approach at present.

The drug discovery process is a time-consuming and quite expensive process. The predictive models of machine learning algorithms have been used efficiently for years in various stages of the drug discovery pipeline. The complexity of these algorithms increases as the size of the molecule increases, adding a single atom to a molecule increases the number of possible combinations. Quantum computers with quantum supremacy can play an important role in complex calculations. Combining the two technologies in practice is a complex endeavor that requires diverse, interdisciplinary teams of scientists working together to be able to integrate the two technologies with the goal of reducing cost and time in drug discovery.

Drug repositioning is a less expensive and time-consuming method than the traditional method of drug discovery. It is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. A key strategy in repositioning approved or investigational drugs is determining the binding affinity of these drugs to target proteins. The large increase in available experimental data has helped deep learning methods to demonstrate superior performance compared to conventional prediction and other traditional computational methods in precise binding affinity prediction. However, these methods are complex and time-consuming, presenting a significant barrier to their development and practical application. In this context, quantum computing (QC) and quantum machine learning (QML) theoretically offer promising solutions to effectively address these challenges. In this work, we introduce a hybrid quantum–classical framework to predict binding affinity. Our approach involves, initially, the implementation of an efficient classical model using convolutional neural networks (CNNs) for feature extraction and three fully connected layers for prediction. Subsequently, retaining the classical module for feature extraction, we implement various quantum and classical modules for binding affinity prediction, which accept the concatenated features as input. Quantum predicted modules are implemented with Variational Quantum Regressions (VQRs), while classical predicted modules are implemented with various fully connected layers. Our findings clearly show that hybrid quantum–classical models accelerate the training process in terms of epochs and achieve faster stabilization. Also, these models demonstrate quantum superiority in terms of complexity, accuracy, and generalization, thereby indicating a promising direction for QML.

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.

Quantum mechanics is a fundamental, axiom-based physical theory that describes and explains phenomena that Classical Mechanics and Electrodynamics are unable to describe. It is considered a cross disciplinary STEM field, advancing the philosophy of Quantum Literacy (QL), which focuses on nature’s real complex problems. QL addresses the challenges of computing skills acquisition, through problem-based activities to offer powerful knowledge to a wide group of learners. Therefore, quantum technology knowledge should be accessible to students and teachers, in a more interactive way. In this paper, we propose contemporary and Problem based scenarios, in which students may acquire stronger mathematical, computational and modelling skills, working as real researchers. Thus, they acquire all necessary knowledge related to quantum principles, such as superposition, teleportation, entanglement, quantum gates and quantum information. The serious games we propose, relate to strategic games, within the computational thinking 2.0 framework. Certain Python libraries and packages are used and the didactic approach is based on game-based inquiry learning.