The growing public and private datasets focused on small molecules screened against biological targets or whole organisms 1 provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design 2-4 , predict on- or off-target effects and create scoring functions 5,6 . This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large datasets and thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague and M. tuberculosis ) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.
The growing public and private datasets focused on small molecules screened against biological targets or whole organisms 1 provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design 2-4 , predict on- or off-target effects and create scoring functions 5,6 . This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large datasets and thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague and M. tuberculosis ) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.
The growing public and private datasets focused on small molecules screened against biological targets or whole organisms 1 provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design 2-4, predict on- or off-target effects and create scoring functions 5,6. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large datasets and thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague and M. tuberculosis) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.
Computational approaches are widely applied in drug discovery to explore properties related to bioactivity, physiochemistry, and toxicology. Over at least the last 20 years, the exploitation of machine learning on molecular data sets has been used to understand the structure–activity relationships that exist between biomolecules and druggable targets. More recently, these methods have also seen application for phenotypic screening data for neglected diseases such as tuberculosis and malaria. Herein, we apply machine learning to build quantum Quantitative Structure Activity Relationship models from antimalarial data sets. There is a continual need for new antimalarials to address drug resistance, and the readily available in vitro data sets could be utilized with newer machine learning approaches as these develop. Furthermore, quantum machine learning is a relatively new method that uses a quantum computer to perform the calculations. First, we present a classical-quantum hybrid computational approach by building a Latent Bernoulli Autoencoder machine learning model for compressing bit-vector descriptors to a size that can be adapted to quantum computers for classification tasks with limited loss of embedded information. Second, we apply our method for feature map compression to quantum classification algorithms, including a completely novel machine learning algorithm with no analogy in classical computers: the Quantum Fourier Transform Classifier. We apply both these approaches to build quantum machine learning models for small-molecule antimalarials with quantum simulation software and then benchmark these quantum models against classical machine learning approaches. While there are many challenges currently facing the development of reliable quantum computers, our results demonstrate that there is potential for the use of this technology in the field of drug discovery.
Quantum machine learning (QML) has been demonstrated to have great potential in revolutionizing the process of drug discovery by dealing with the problem of high computational complexity of molecular simulations. Conventional computational techniques are not very effective in predicting the properties of molecules and pharmaceutical activity because the simulation of large molecules is too complex. This paper will discuss how QML algorithms can be used, specifically variational quantum circuits and quantum kernel methods, to improve the predictability of molecular properties and accelerate the process of drug discovery. To enhance the prediction accuracy, the research use quantum-enhanced feature extraction together with classical machine learning technologies. Research methodology is applied to datasets of molecular structure and biological activity in order to provide a more useful model to learn about how molecular features relate to biological outcomes using quantum machine learning models. These results suggest that it is 15 % more accurate in prediction and 20 % faster in computing high-dimensional molecular simulations compared to standard machine learning algorithms. The efficiency of the QML-based method is statistically proven with an accuracy 89%, precision 87%, and an F1-score of 0.91. The results point to the possible ability of QML to decrease the time and computational resources spent on the exploration of drug discovery, providing a new direction for the improvement of molecular property prediction. The study is a contribution to the emerging area of QML in pharmaceutical use and demonstrates that it can simplify drug discovery and enhance the accuracy with which predictions are made. The paper demonstrates that QML may produce a significant impact on the drug development process by simulating the molecular properties faster, more effectively, and more accurately.
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.
Machine learning (ML), a subset of artificial intelligence (AI), has transformed biological applications. In biological science, ML-based algorithms have also successfully handled large amounts of data. The use of ML-based AI approaches has switched applications from generalization to customization. Some recent trends in ML-/AI-based systems include recommendation systems, healthcare chatbots, and precision medicine, and achieving computational speed is always a challenge in such instances. The ML model training can be accelerated using quantum computing (QC), and more effective algorithms can be produced. QC can create an AI system that is optimized and capable of quickly finishing years of analysis. Theoretically, quantum computers could indeed efficiently simulate the entire problem, including interactions at the atomic level. Additionally, algorithmic advancements in quantum machine learning (QML) provide intriguing alternatives to traditional ML strategies that might be beneficial for the biochemical activities associated with the initial stages of drug discovery. Likewise, quantum hardware is also rapidly evolving to the point where perfect simulations are challenging to achieve even with the world’s most powerful supercomputers. This chapter will cover the classical machine learning techniques as well as the core concepts of quantum computing with respect to ML and DL. It will discuss the applications of quantum computing with respect to biological applications such as drug discovery, protein function prediction, and predicting interactions between drugs and targets. The chapter also includes several open-source tools which can be employed for quantum computing in different biological applications. It will also discuss various challenges related to quantum computing and its future prospects while dealing with complex biological problems.
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