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Research papers on AI for drug discovery

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  2. Artificial intelligence to deep learning: machine intelligence approach for drug discovery

    Rohan V. Gupta, Devesh Srivastava, Mehar Sahu, et al. · 2021 · Molecular Diversity · 1,459 citations

    Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in severa

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

    Xin Yu Yang, Yifei Wang, Ryan Byrne, et al. · 2019 · Chemical Reviews · 1,023 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

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  5. 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 · 460 citations

    Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthe

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  6. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

    Anita Ioana Vișan, Irina Neguț · 2024 · Life · 281 citations

    Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizi

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  7. Artificial intelligence for drug discovery: Resources, methods, and applications

    Wei Chen, Xuesong Liu, Sanyin Zhang, et al. · 2023 · Molecular Therapy — Nucleic Acids · 275 citations

    Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the mol

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  8. Artificial Intelligence for Drug Discovery: Are We There Yet?

    Catrin Hasselgren, Tudor Ionel Oprea · 2023 · The Annual Review of Pharmacology and Toxicology · 230 citations

    Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning,

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  9. Artificial intelligence in drug discovery: applications and techniques

    Jianyuan Deng, Zhibo Yang, Iwao Ojima, et al. · 2021 · Briefings in Bioinformatics · 222 citations

    Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e. molecular property prediction and molecule generation. We then present common data resources, molecule representations and benchmark platforms. As a major part of the survey, AI techniques are dissected into model architectures and learning paradigms. To reflect the technical development of AI in drug discovery over the years, the surveyed

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

    A. Ocana, A. Pandiella, Cristian Privat, et al. · 2025 · Biomarker Research · 171 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

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

    Chen Fu, Qiuchen Chen · 2025 · Journal of Pharmaceutical Analysis · 159 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

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

    Amit Gangwal, Antonio Lavecchia · 2025 · Journal of Medicinal Chemistry · 123 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

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

    Jiangyan Zhang, Haolin Li, Yun-Cong Zhang, et al. · 2025 · Briefings in Bioinformatics · 107 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

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

    Eren Ogut · 2025 · Clinics and Practice · 98 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

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  19. Explainable Artificial Intelligence: A Perspective on Drug Discovery

    Yazdan Ahmad Qadri, Sibhghatulla Shaikh, Khursheed Ahmad, et al. · 2025 · Pharmaceutics · 64 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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  20. Explainable Artificial Intelligence in Drug Discovery: Bridging Predictive Power and Mechanistic Insight

    Antonio Lavecchia · 2025 · Wiley Interdisciplinary Reviews: Computational Molecular Science · 57 citations

    Explainable artificial intelligence (XAI) is increasingly essential in drug discovery, where interpretability and trust must accompany predictive accuracy. As deep learning models, particularly, deep neural networks (DNNs) and graph neural networks (GNNs), enhance molecular property prediction, de novo design, and toxicity estimation, transparent, mechanistically meaningful insights become critical. This article classifies major XAI strategies in computational molecular science, including gradient‐based attribution, perturbation analysis, surrogate modeling, counterfactual reasoning, and self‐explaining architectures. Molecular representations, such as fingerprints, SMILES, molecular graphs,

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  21. The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials

    Vera Malheiro, Beatriz Santos, Ana Figueiras, et al. · 2025 · Pharmaceuticals · 56 citations

    Artificial intelligence (AI) is a subfield of computer science focused on developing systems that can execute tasks traditionally associated with human intelligence. AI systems work through algorithms based on rules or instructions that enable the machine to make decisions. With the advancement of science, more sophisticated AI techniques, such as machine learning and deep learning, have been developed, allowing machines to learn from large amounts of data and improve their performance over time. The pharmaceutical industry has greatly benefited from the development of this technology. AI has revolutionized drug discovery and development by enabling rapid and effective analysis of vast volum

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

    Shashi Kant, Deepika, Saheli Roy · 2025 · Discover Pharmaceutical Sciences · 54 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

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  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 · 53 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

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  24. Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes

    Sarfaraz K. Niazi · 2025 · Pharmaceuticals · 49 citations

    Artificial intelligence (AI) is emerging as a valuable complementary tool in small-molecule drug discovery, augmenting traditional methodologies rather than replacing them. This review examines the evolution of AI from early rule-based systems to advanced deep learning, generative models, diffusion models, and autonomous agentic AI systems, highlighting their applications in target identification, hit discovery, lead optimization, and safety prediction. We present both successes and failures to provide a balanced perspective. Notable achievements include baricitinib (BenevolentAI/Eli Lilly, an existing drug repurposed through AI-assisted analysis for COVID-19 and rheumatoid arthritis), halic

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