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Research papers on AI for drug discovery

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  1. Artificial Intelligence Drug Discovery and Development

    Subedar Ikra A, Bhandare Sangita N. · 2025 · Asian Journal of Pharmaceutical Research and Development · 1,591 citations

    The integration of Artificial Intelligence (AI) into drug discovery and development has revolutionized the pharmaceutical landscape by enabling faster, cost-effective, and data-driven innovations. Traditional drug discovery methods are time-consuming and expensive, often requiring over a decade and billions of dollars to bring a new drug to market. AI technologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) significantly enhance each stage of the drug discovery pipeline—from target identification and virtual screening to lead optimization, toxicity prediction, and clinical trial design. By analyzing complex biological, chemical, and clinical datas

  3. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

    Xin Yang, Yifei Wang, Ryan Byrne, et al. · 2019 · Chemical Reviews · 1,030 citations

    Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, inc

  6. Artificial intelligence in drug discovery: recent advances and future perspectives

    José Jiménez-Luna, Francesca Grisoni, Nils Weskamp, et al. · 2021 · Expert Opinion on Drug Discovery · 465 citations

    : Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.

  7. Big Data and Artificial Intelligence Modeling for Drug Discovery

    Hao Zhu · 2019 · The Annual Review of Pharmacology and Toxicology · 446 citations

    Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mini

  8. Artificial intelligence in cancer target identification and drug discovery

    Yujie You, Xin Lai, Yi Pan, et al. · 2022 · Signal Transduction and Targeted Therapy · 420 citations

    Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we

  10. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

    Dolores R. Serrano, Francis C. Luciano, Brayan J. Anaya, et al. · 2024 · Pharmaceutics · 402 citations

    Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, moni

  13. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach

    Alberto Ocana, A. Pandiella, Cristian Privat, et al. · 2025 · Biomarker Research · 140 citations

    Artificial intelligence (AI) can transform drug discovery and early drug development by addressing inefficiencies in traditional methods, which often face high costs, long timelines, and low success rates. In this review we provide an overview of how to integrate AI to the current drug discovery and development process, as it can enhance activities like target identification, drug discovery, and early clinical development. Through multiomics data analysis and network-based approaches, AI can help to identify novel oncogenic vulnerabilities and key therapeutic targets. AI models, such as AlphaFold, predict protein structures with high accuracy, aiding druggability assessments and structure-ba

  14. The future of pharmaceuticals: Artificial intelligence in drug discovery and development

    Chen Fu, Qiuchen Chen · 2025 · Journal of Pharmaceutical Analysis · 129 citations

    Artificial Intelligence (AI) is revolutionizing traditional drug discovery and development models by seamlessly integrating data, computational power, and algorithms. This synergy enhances the efficiency, accuracy, and success rates of drug research, shortens development timelines, and reduces costs. Coupled with machine learning (ML) and deep learning (DL), AI has demonstrated significant advancements across various domains, including drug characterization, target discovery and validation, small molecule drug design, and the acceleration of clinical trials. Through molecular generation techniques, AI facilitates the creation of novel drug molecules, predicting their properties and activitie

  15. Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives

    Amit Gangwal, Antonio Lavecchia · 2025 · Journal of Medicinal Chemistry · 109 citations

    Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due to limited data utilization, now faces urgent demands for accelerated lead compound identification. Innovations in biological data and computational chemistry have spurred a shift from trial-and-error methods to holistic approaches to medicinal chemistry. Computational techniques, particularly artificial intelligence (AI), notably machine learning (ML) and deep learning (DL), have revolutionized drug development, enhancing data analysis and predictive modeling. Natural products (NPs) have long served as rich sources of biologically active compou

  17. Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery

    Eren Ogut · 2025 · Clinics and Practice · 78 citations

    Aims/Background: The growing integration of artificial intelligence (AI) into clinical medicine has opened new possibilities for enhancing diagnostic accuracy, therapeutic decision-making, and biomedical innovation across several domains. This review is aimed to evaluate the clinical applications of AI across five key domains of medicine: diagnostic imaging, clinical decision support systems (CDSS), surgery, pathology, and drug discovery, highlighting achievements, limitations, and future directions. Methods: A comprehensive PubMed search was performed without language or publication date restrictions, combining Medical Subject Headings (MeSH) and free-text keywords for AI with domain-specif

  19. Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction

    Jiangyan Zhang, Haolin Li, Yuncong Zhang, et al. · 2025 · Briefings in Bioinformatics · 61 citations

    Abstract Toxicity risk assessment plays a crucial role in determining the clinical success and market potential of drug candidates. Traditional animal-based testing is costly, time-consuming, and ethically controversial, which has led to the rapid development of computational toxicology. This review surveys over 20 ADMET prediction platforms, categorizing them into rule/statistical-based methods, machine learning (ML) methods, and graph-based methods. We also summarize major toxicological databases into four types: chemical toxicity, environmental toxicology, alternative toxicology, and biological toxin databases, highlighting their roles in model training and validation. Furthermore, we rev

  21. Artificial intelligence in drug discovery and development: transforming challenges into opportunities

    Shashi Kant, Deepika, Saheli Roy · 2025 · Discover Pharmaceutical Sciences · 46 citations

    Artificial intelligence (AI) has revolutionized drug discovery and development by accelerating timelines, reducing costs, and increasing success rates. AI leverages machine learning (ML), deep learning (DL), and natural language processing (NLP) to analyze vast datasets, enabling the rapid identification of drug targets, prediction of compound efficacy, and optimization of drug design. It accelerates lead discovery by predicting pharmacokinetics, toxicity, and potential side effects while also refining clinical trial designs through improved patient recruitment and data analysis. This review highlights the diverse benefits of AI in drug development, including enhanced efficiency, greater acc

  22. Role Of Artificial Intelligence In Cancer Drug Discovery And Development.

    Sruthi Sarvepalli, Shubhadeepthi Vadarevu · 2025 · Cancer letters · 42 citations

    The role of artificial intelligence (AI) in cancer drug discovery and development has garnered significant attention due to its potential to transform the traditionally time-consuming and expensive processes involved in bringing new therapies to market. AI technologies, such as machine learning (ML) and deep learning (DL), enable the efficient analysis of vast datasets, facilitate faster identification of drug targets, optimization of compounds, and prediction of clinical outcomes. This review explores the multifaceted applications of AI across various stages of cancer drug development, from early-stage discovery to clinical trial design, development. In early-stage discovery, AI-driven meth

  23. Artificial intelligence revolution in drug discovery: A paradigm shift in pharmaceutical innovation.

    Somayah J. Jarallah, Fahad A. Almughem, Nada K. Alhumaid, et al. · 2025 · International journal of pharmaceutics · 42 citations

    Integrating artificial intelligence (AI) into drug discovery has revolutionized pharmaceutical innovation, addressing the challenges of traditional methods that are costly, time-consuming, and suffer from high failure rates. By utilizing machine learning (ML), deep learning (DL), and natural language processing (NLP), AI enhances various stages of drug development, including target identification, lead optimization, de novo drug design, and drug repurposing. AI tools, such as AlphaFold for protein structure prediction and AtomNet for structure-based drug design, have significantly accelerated the discovery process, improved efficiency and reduced costs. Success stories like Insilico Medicine

  24. Explainable Artificial Intelligence: A Perspective on Drug Discovery

    Yazdan Ahmad Qadri, Sibhghatulla Shaikh, Khursheed Ahmad, et al. · 2025 · Pharmaceutics · 40 citations

    The convergence of artificial intelligence (AI) and drug discovery is accelerating the pace of therapeutic target identification, refining of drug candidates, and streamlining processes from laboratory research to clinical applications. Despite these promising advances, the inherent opacity of AI-driven models, especially deep-learning (DL) models, poses a significant “black-box" problem, limiting interpretability and acceptance within the pharmaceutical researchers. Explainable artificial intelligence (XAI) has emerged as a crucial solution for enhancing transparency, trust, and reliability by clarifying the decision-making mechanisms that underpin AI predictions. This review systematically

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