Machine Learning Applications in Drug Discovery

Drug discovery and manufacturing are adequately time-consuming, complicated, and costly processes that depend on several parameters. Machine learning (ML) is becoming increasingly popular in drug discovery and manufacturing by yielding promising outcomes. ML techniques offer a collection of tools for improving the drug discovery and decision-making processes, with the utilization of large amounts of high-quality pharmaceutical data in a variety of applications such as de novo drug designs, hit discoveries, and QSAR analysis, to obtain reliable results. ML can be used at any step of the drug discovery and manufacturing process. Identification of predictive biomarkers, validation of drug targets, and examination of digital pathology information in clinical trials are just a few examples. The context and methodology of the ML applications are varied, with some generating precise forecasts and insights. As ML techniques are increasingly used, so do their limitations become more apparent. Such constraints include the necessity for big data, data scarcity, and the inability to evaluate and repeat the ML results. It's also becoming clear that the ML procedures aren't completely self-contained, thus necessitating the retraining of pharmacological data, even after the deployment of the ML results. There is still a huge demand to generate systematic and comprehensive high-dimensional pharmacological data in all sectors. Some factors for increasing the ML results include prognostic biomarkers, target validation, and digital pathology. The ML challenges must address the major cause of insufficiency in interpretability outcomes, which restrict their applications in drug discovery and manufacturing. To solve several challenges in validating ML algorithms and improving decision-making, clinical trials require absolute and methodological data. The use of ML can enhance data-dependent assessment making and accelerate drug discovery and manufacturing processes while lowering failure rates. This book chapter summarizes the recent literature on ML tools/methods used in drug discovery and manufacturing, which are used at each phase of drug development to speed up research and reduce risk and cost in clinical trials. The advanced innovative ML techniques to overcome some of these obstacles and their potential application in drug discovery are described with examples derived from drug discovery and related fields. The ML techniques discussed herein are expected to increase the ML roles in drug discovery and manufacturing processes to a new level with the aid of advanced computer intelligence.

Machine learning (ML) stands at the frontier of technological advancement across various domains, exhibiting both novel applications and significant enhancements to existing systems. This paper explores the integration of ML in diverse fields, including physics, customer service, geosciences, drug discovery, and smart systems, detailing how these innovations are redefining the capabilities of each sector. In physics, ML has paved the way for new methods such as symbolic regression, which have revolutionized theoretical understanding and experimental applications. In the realm of customer service, AI-driven chatbots have transformed user interactions, offering both improved compliance with user needs and enhanced service quality. Geosciences have benefited from ML in remote sensing and environmental monitoring, where predictive models and data analytics have led to more accurate forecasting and resource management. Furthermore, the integration of ML in drug discovery has accelerated the identification of novel compounds and streamlined the development of new medications, significantly reducing both the time and cost associated with traditional methods. In smart systems, particularly those utilizing Internet of Things (IoT) and 5G technologies, ML has been instrumental in advancing automation and connectivity, thereby enhancing system efficiency and effectiveness. This paper will delve into the specific ML techniques employed in these fields, analyze their impacts, and discuss the potential future directions of ML applications. By providing a comprehensive review of ML frameworks and addressing the associated challenges and ethical considerations, the paper aims to present a holistic view of the pervasive influence of ML across varied disciplines.

Machine learning has emerged as a powerful tool in the field of drug discovery and development, revolutionizing the way pharmaceutical research is conducted. This abstract provides a concise overview of the key applications and impacts of machine learning in this domain. Drug discovery and development is a complex, time-consuming, and costly process that traditionally relies on trial-and-error experimentation. Machine learning, with its ability to analyze vast datasets and extract meaningful insights, has significantly accelerated and optimized various aspects of this process. One crucial application of machine learning in drug discovery is the prediction of potential drug candidates. Machine learning models can also aid in the identification of biomarkers for diseases, enabling more targeted drug development. In the clinical trial phase, machine learning algorithms assist in patient selection, monitoring, and optimization of trial protocols. Predictive models can identify patient subpopulations most likely to respond to a particular treatment, leading to more efficient trials and better patient outcomes. Additionally, machine learning has streamlined drug repurposing efforts by identifying existing drugs with potential new applications. This approach has the potential to save significant time and resources by leveraging existing safety and efficacy data. Furthermore, machine learning enhances the drug development pipeline by optimizing drug formulation and dosage, predicting adverse reactions, and assisting in regulatory compliance. While machine learning offers tremendous promise in drug discovery and development, it also presents challenges related to data quality, model interpretability, and regulatory approval. Addressing these challenges will be crucial for maximizing the potential of machine learning in the pharmaceutical industry.

This review critically examines the integration of Machine Learning (ML) in drug discovery, highlighting its applications across target identification, hit discovery, lead optimization, and predictive toxicology. Despite ML's potential to revolutionize drug discovery through enhanced efficiency, predictive accuracy, and novel insights, significant challenges persist. These include issues related to data quality, model interpretability, integration into existing workflows, and regulatory and ethical considerations. The review advocates for advancements in algorithmic approaches, interdisciplinary collaboration, improved data-sharing practices, and evolving regulatory frameworks as potential solutions to these challenges. By addressing these hurdles and leveraging the capabilities of ML, the drug discovery process can be significantly accelerated, paving the way for the development of new therapeutics. This review calls for continued research, collaboration, and dialogue among stakeholders to realize the transformative potential of ML in drug discovery fully.
 Keywords: Machine Learning, Drug Discovery, Predictive Toxicology, Data Quality, Interdisciplinary Collaboration.

The process of drug discovery and development is a complex, time-consuming, and costly endeavor. However, the integration of machine learning (ML) and Artificial Intelligence (AI) has revolutionized the pharmaceutical industry by providing innovative solutions to challenging problems. This review highlights the role of ML in drug discovery, including target identification, lead optimization, and drug design. ML-driven approaches, such as deep learning and neural networks, have accelerated the discovery process, reducing the time and expense involved in traditional drug development. Computational tools and software have made the drug research and development process more convenient, enabling the use of online screening, structure-based design, and lead optimization. The application of ML in drug discovery has the potential to transform the pharmaceutical industry, enabling the development of novel and effective therapies for various diseases. This review aims to provide an overview of the current state of ML in drug discovery, highlighting its applications, advantages, and future directions.

Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on "Big Data", machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future. Traditional areas like virtual screening, library design and high-throughput screening analysis are highlighted in this review. Applying machine learning in drug discovery is an area that has become very important. Applications of machine learning in early drug discovery has been extended from predicting ADME properties and target activity to tasks like de novo molecular design and prediction of chemical reactions.

Artificial intelligence and machine learning in drug discovery and development

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

Polymeric membranes have become essential for energy-efficient gas separations such as natural gas sweetening, hydrogen separation, and carbon dioxide capture. Polymeric membranes face challenges like permeability-selectivity tradeoffs, plasticization, and physical aging, limiting their broader applicability. Machine learning (ML) techniques are increasingly used to address these challenges. This review covers current ML applications in polymeric gas separation membrane design, focusing on three key components: polymer data, representation methods, and ML algorithms. Exploring diverse polymer datasets related to gas separation, encompassing experimental, computational, and synthetic data, forms the foundation of ML applications. Various polymer representation methods are discussed, ranging from traditional descriptors and fingerprints to deep learning-based embeddings. Furthermore, we examine diverse ML algorithms applied to gas separation polymers. It provides insights into fundamental concepts such as supervised and unsupervised learning, emphasizing their applications in the context of polymer membranes. The review also extends to advanced ML techniques, including data-centric and model-centric methods, aimed at addressing challenges unique to polymer membranes, focusing on accurate screening and inverse design.

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery

BackgroundAnxiety is a pervasive mental health disorder with severe implications for individual wellbeing and societal productivity. The contemporary rise of anxiety, particularly among youth in digitally-saturated environments, underscores a critical need for advanced predictive tools to facilitate early intervention and mitigation. While machine learning (ML) holds significant promise in this domain, a comprehensive synthesis of its application in anxiety prediction, along with a critical evaluation of methodological trends and gaps, is only emerging in the literature. The main idea of the current systematic review is to bridge the understanding of current ML applications in mental health with the critical needs for enhanced diagnostic precision, personalized interventions and prevention.ObjectivesThis systematic review aims to systematically synthesize research on ML approaches to predicting anxiety, critically evaluating the algorithms, features, and validation techniques employed across studies. The objective is to identify prevailing ML techniques, assess their performance, and highlight crucial methodological trends, existing gaps, and their implications for effective early intervention and real-world deployment.Eligibility criteriaStudies included had to apply machine learning techniques to predict anxiety or its severity using either clinical or behavioral datasets. Exclusion criteria included non-English language papers, reviews, older or previously reviewed publications, and those not specifically targeting anxiety. We focus on questionnaire research, but also discuss multimodal fusion techniques.Information sourcesWe searched the Scopus database and Google Scholar for articles published between 2018 and 2025 using combinations of keywords including “anxiety prediction,” “machine learning,” and “mental health.” The last search was conducted in July 2025.Risk of biasStudies were screened in two phases: (1) by verifying the presence of relevant keywords in the main body, and (2) by reviewing title, introduction, and conclusion to ensure alignment with anxiety prediction via ML. Studies relying solely on self-reported metrics or with unclear algorithmic transparency were noted for potential bias.ResultsA total of 19 studies were included, encompassing 44, 608 participants. GAD-7 and DASS-21 were the most commonly used diagnostic instruments. ML techniques such as Random Forest and Gradient Boosting achieved the highest predictive accuracy, with some studies reporting up to 98% accuracy. Metrics like F1-score, AUC, and specificity were commonly reported.Limitations of evidenceExisting studies display a range of methodological and conceptual limitations that constrain their generalizability and clinical utility. The review identified significant methodological limitations hindering generalizability and clinical utility, including reliance on small, homogeneous samples, which raises concerns about overfitting and population bias. Furthermore, common issues include a lack of external validation, inconsistent evaluation metrics, and the “black-box” nature of many ML algorithms, which impedes clinical trust and adoption.InterpretationThe findings support the effectiveness of machine learning for anxiety detection and prediction, particularly in early intervention contexts. The integration of explainable ML and diverse, clinically validated data is necessary for real-world deployment. The existing body of research also shows a notable scarcity in studies predicting anxiety before symptom manifestation. These insights emphasize the critical need for integrating explainable ML (XAI) and utilizing diverse, clinically validated datasets to enable real-world deployment and proactive mental health support.