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Papers on “artificial intelligence 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 · 1,660 cites

   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

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

   Rohan Gupta, Devesh Srivastava, Mehar Sahu, et al. · 2021 · Molecular Diversity · 1,472 cites

   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

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

   Xin Yang, Yifei Wang, Ryan Byrne, et al. · 2019 · Chemical Reviews · 1,060 cites

   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

4. Advancing Drug Discovery via Artificial Intelligence

   H. C. Stephen Chan, Hanbin Shan, Thamani Dahoun, et al. · 2019 · Trends in Pharmacological Sciences · 657 cites

5. How artificial intelligence is changing drug discovery

   Nic Fleming · 2018 · Nature · 558 cites

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 · 487 cites

   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

7. Big Data and Artificial Intelligence Modeling for Drug Discovery

   Hao Zhu · 2019 · The Annual Review of Pharmacology and Toxicology · 459 cites

   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 (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

   Dolores R. Serrano, Francis C. Luciano, Brayan J. Anaya, et al. · 2024 · Pharmaceutics · 458 cites

   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