TY - JOUR
T1 - AI in drug discovery and its clinical relevance
AU - Qureshi, Rizwan
AU - Irfan, Muhammad
AU - Gondal, Taimoor Muzaffar
AU - Khan, Sheheryar
AU - Wu, Jia
AU - Hadi, Muhammad Usman
AU - Heymach, John
AU - Le, Xiuning
AU - Yan, Hong
AU - Alam, Tanvir
N1 - Publisher Copyright:
© 2023 The Author(s)
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Biotechnology
KW - Drug discovery
KW - Graph neural networks
KW - Molecular dynamics simulation
KW - Molecule representation
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85166011357&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2023.e17575
DO - 10.1016/j.heliyon.2023.e17575
M3 - Article
AN - SCOPUS:85166011357
SN - 2405-8440
VL - 9
JO - Heliyon
JF - Heliyon
IS - 7
M1 - e17575
ER -