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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 Gupta, Devesh Srivastava, Mehar Sahu, et al. · 2021 · Molecular Diversity · 1,453 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

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

    Xin Yang, Yifei Wang, Ryan Byrne, et al. · 2019 · Chemical Reviews · 1,046 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 · 478 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

  7. Big Data and Artificial Intelligence Modeling for Drug Discovery

    Hao Zhu · 2019 · The Annual Review of Pharmacology and Toxicology · 449 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

  9. Artificial intelligence in cancer target identification and drug discovery

    Yujie You, Xin Lai, Yi Pan, et al. · 2022 · Signal Transduction and Targeted Therapy · 427 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 · 423 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

  20. Artificial Intelligence (AI) in Drug Discovery and Medicine

    Ahuja Varun · 2019 · Journal of Clinical Cases & Reports

    Artificial intelligence (AI) is a branch of computer science that deals with the development of algorithms that seek to simulate human intelligence. The phrase “artificial intelligence” was likely coined during a conference at Dartmouth College in 1956. The earliest work of medical AI dates back to the early 1970s. Over years, AI has found implications in various fields. In this article, we summarize its applications in drug discovery and medicine.

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