AI in drug discovery and its clinical relevance

Rizwan Qureshi, Muhammad Irfan, Taimoor Muzaffar Gondal, Sheheryar Khan, Jia Wu, Muhammad Usman Hadi, John Heymach, Xiuning Le, Hong Yan, Tanvir Alam

Research output: Contribution to journalReview articlepeer-review

32 Citations (Scopus)
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Abstract

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.
Original languageEnglish
Article numbere17575
Pages (from-to)1-23
Number of pages23
JournalHeliyon
Volume9
Issue number7
Early online date28 Jun 2023
DOIs
Publication statusPublished (in print/issue) - 31 Jul 2023

Bibliographical note

Funding Information:
The open access publication of this article was funded by the Qatar National Library (QNL), Qatar.

Funding Information:
Dr. Xiuning Le is an Assistant Professor, Department of Thoracic Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas, MD Anderson Cancer Center, Houston, USA. She received her PhD from Harvard Medical School, Boston, MA, USA, in Biological and Biomedical Sciences and postdoctoral training from Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA. She received funding from American Society of Clinical Oncology (ASCO), Claudia Adams Barr Program for Innovative Cancer Research, and National Cancer Institute.

Funding Information:
This work is supported by Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA), Hong Kong Research Grants Council (Project 11204821 ), College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar , and Qatar National Research Fund (QNRF) Grant TDF 03-1206-210011 and RRC02-0805-210019 to Tanvir Alam.

Publisher Copyright:
© 2023 The Author(s)

Keywords

  • Artificial intelligence
  • Biotechnology
  • Graph neural networks
  • Molecule representation
  • Reinforcement learning
  • Drug discovery
  • Molecular dynamics simulation

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