AI in drug discovery and its clinical relevance

被引:75
作者
Qureshi, Rizwan [1 ,5 ]
Irfan, Muhammad [2 ]
Gondal, Taimoor Muzaffar [3 ]
Khan, Sheheryar [4 ]
Wu, Jia [5 ]
Hadi, Muhammad Usman [6 ]
Heymach, John [7 ,8 ]
Le, Xiuning [7 ,8 ]
Yan, Hong
Alam, Tanvir [1 ]
机构
[1] Hamad Bin Khalifa Univ, Coll Sci & Engn, Doha, Qatar
[2] Ghulam Ishaq Khan Inst Engn Sci & Technol, Fac Elect Engn, Swabi, Pakistan
[3] Super Univ, Fac Engn & Technol, Lahore 54000, Pakistan
[4] Hong Kong Polytech Univ, Sch Profess Educ & Execut Dev, Hong Kong, Peoples R China
[5] Univ Texas MD Anderson Canc Ctr, Dept Imaging Phys, Houston, TX 77030 USA
[6] Ulster Univ, Sch Engn, Belfast, North Ireland
[7] Univ Texas MD Anderson Canc Ctr, Div Canc Med, Dept Thorac Head & Neck Med Oncol, Houston, TX USA
[8] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
关键词
Artificial intelligence; Biotechnology; Graph neural networks; Molecule representation; Reinforcement learning; Drug discovery; Molecular dynamics simulation; MOLECULAR-DYNAMICS SIMULATIONS; PROTEIN-STRUCTURE PREDICTION; ARTIFICIAL-INTELLIGENCE; PHARMACEUTICAL-INDUSTRY; DESIGN; MODELS; IDENTIFICATION; REPRESENTATIONS; OPTIMIZATION; PERFORMANCE;
D O I
10.1016/j.heliyon.2023.e17575
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:23
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