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Curated Papers > High-impact authoritative literature on "AI Drug Discovery"

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

Literature Information ​

DOI10.1080/17460441.2021.1909567
PMID33779453
JournalExpert opinion on drug discovery
Impact Factor4.9
JCR QuartileQ1
Publication Year2021
Times Cited104
KeywordsDrug discovery, QSAR, artificial intelligence, de novo drug design, synthesis prediction
Literature TypeJournal Article, Research Support, Non-U.S. Gov't, Review
ISSN1746-0441
Pages949-959
Issue16(9)
AuthorsJosé Jiménez-Luna, Francesca Grisoni, Nils Weskamp, Gisbert Schneider

TL;DR ​

This paper reviews the current status of artificial intelligence, particularly deep learning, in chemoinformatics and its applications in drug discovery, highlighting advancements in modeling techniques and the gradual reduction of skepticism in the pharmaceutical field. The findings emphasize the potential of innovative methodologies and open data sharing to address significant challenges in drug development, marking a transformative shift in medicinal chemistry.

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Drug discovery · QSAR · artificial intelligence · de novo drug design · synthesis prediction

Abstract ​

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 synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: 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.

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Primary Questions Addressed ​

  1. How can AI improve the accuracy of predictive models in drug discovery?
  2. What are the specific limitations of current deep learning methods in medicinal chemistry?
  3. In what ways can open data sharing enhance collaboration in AI-driven drug discovery?
  4. How do recent advances in machine learning paradigms influence the efficiency of chemical synthesis prediction?
  5. What role does hybrid de novo design play in addressing complex challenges in drug discovery?

Key Findings ​

Key Insights ​

  1. Research Background and Purpose: The study addresses the transformative role of artificial intelligence (AI) in drug discovery, particularly through the lens of machine learning and deep learning technologies. Historically, there was skepticism surrounding AI's applicability in pharmaceutical discovery; however, recent advancements in computing capabilities and algorithms have shifted this perspective. The purpose of the research is to review the current state of AI in chemoinformatics and explore its potential to enhance medicinal chemistry.

  2. Major Methods and Findings: The review covers several key areas of AI application in drug discovery:

    • Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) and Structure-Based Modeling: These methods allow for predictive modeling of molecular behavior and interactions.
    • De Novo Molecular Design: AI is utilized in generating novel compounds with desired biological activity.
    • Chemical Synthesis Prediction: AI models predict feasible synthetic routes for new compounds. The findings emphasize that while deep learning techniques have made significant strides in addressing drug discovery challenges, certain limitations remain. The study highlights innovative methodologies such as message-passing models and spatial-symmetry-preserving networks that are emerging to tackle complex problems in the field.

  3. Core Conclusions: The research concludes that AI, particularly through deep learning, has the potential to revolutionize drug discovery by providing advanced predictive capabilities and facilitating the design of new therapeutic compounds. However, the authors note that many fundamental issues in drug discovery are still in their infancy regarding resolution through AI. The integration of next-generation AI approaches, including hybrid design frameworks and comprehensive data-sharing practices, is essential for further progress.

  4. Research Significance and Impact: This research underscores the significance of AI in enhancing drug discovery processes, which could lead to expedited development timelines and more effective therapies. The emphasis on open data sharing and collaborative model development suggests a paradigm shift towards more transparent and efficient research practices within the pharmaceutical industry. Ultimately, this work could pave the way for improved drug discovery methodologies, potentially resulting in breakthroughs in treating various diseases and improving patient outcomes. The insights from this review will be crucial for researchers and industry professionals aiming to leverage AI technologies for future drug development endeavors.

Literatures Citing This Work ​

  1. Phytochemical Constituents of Medicinal Plants for the Treatment of Chronic Inflammation. - Ki Sung Kang - Biomolecules (2021)
  2. Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking. - Philipe Oliveira Fernandes;Diego Magno Martins;Aline de Souza Bozzi;João Paulo A Martins;Adolfo Henrique de Moraes;Vinícius Gonçalves Maltarollo - Molecular diversity (2021)
  3. A critical overview of computational approaches employed for COVID-19 drug discovery. - Eugene N Muratov;Rommie Amaro;Carolina H Andrade;Nathan Brown;Sean Ekins;Denis Fourches;Olexandr Isayev;Dima Kozakov;José L Medina-Franco;Kenneth M Merz;Tudor I Oprea;Vladimir Poroikov;Gisbert Schneider;Matthew H Todd;Alexandre Varnek;David A Winkler;Alexey V Zakharov;Artem Cherkasov;Alexander Tropsha - Chemical Society reviews (2021)
  4. The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes. - Douglas B Kell - Molecules (Basel, Switzerland) (2021)
  5. Intelligent host engineering for metabolic flux optimisation in biotechnology. - Lachlan J Munro;Douglas B Kell - The Biochemical journal (2021)
  6. Evaluating High-Variance Leaves as Uncertainty Measure for Random Forest Regression. - Thomas-Martin Dutschmann;Knut Baumann - Molecules (Basel, Switzerland) (2021)
  7. DockStream: a docking wrapper to enhance de novo molecular design. - Jeff Guo;Jon Paul Janet;Matthias R Bauer;Eva Nittinger;Kathryn A Giblin;Kostas Papadopoulos;Alexey Voronov;Atanas Patronov;Ola Engkvist;Christian Margreitter - Journal of cheminformatics (2021)
  8. Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions. - Ashwin Dhakal;Cole McKay;John J Tanner;Jianlin Cheng - Briefings in bioinformatics (2022)
  9. MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra. - Aditya Divyakant Shrivastava;Neil Swainston;Soumitra Samanta;Ivayla Roberts;Marina Wright Muelas;Douglas B Kell - Biomolecules (2021)
  10. "Compoundless Anaesthesia", Controlled Administration, and Post-Operative Recovery Acceleration: Musings on Theoretical Nanomedicine Applications. - Tyler Lance Jaynes - Journal of clinical medicine (2022)

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