Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

被引:0
作者
Nehmeh, Bilal [1 ]
Rebehmed, Joseph [2 ]
Nehmeh, Riham [3 ]
Taleb, Robin [4 ]
Akoury, Elias [1 ]
机构
[1] Lebanese Amer Univ, Dept Phys Sci, Beirut 11022801, Lebanon
[2] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut 11022801, Lebanon
[3] INSA Rennes, Inst Elect & Telecommun Rennes IETR, UMR 6164, F-35708 Rennes, France
[4] Lebanese Amer Univ, Dept Phys Sci, Byblos Campus,4M8F 6QF, Blat, Lebanon
关键词
neurodegenerative diseases; artificial intelligence; drug discovery; machine learning; pathological hallmarks; INTERACTION PREDICTION; TYROSINE KINASE; NEURAL-NETWORKS; PEMBROLIZUMAB; CHEMOTHERAPY; GENERATION; INHIBITOR; MOLECULE; MODELS; ROSUVASTATIN;
D O I
10.1016/j.drudis.2024.104216
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these challenges. This review explores the application of AI techniques to unravel therapeutic frontiers for NDs. We examine the current landscape of AI-driven drug discovery and discuss the potentials of AI in accelerating the identification of novel therapeutic targets on ND research and drug development, optimization of drug candidates, and expediating personalized medicine approaches. Finally, we outline future directions and challenges in harnessing AI for the advancement of therapeutics in this critical area by emphasizing the importance of interdisciplinary collaboration and ethical considerations.
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页数:16
相关论文
共 156 条
[91]   Causative Genes in Amyotrophic Lateral Sclerosis and Protein Degradation Pathways: a Link to Neurodegeneration [J].
Maurel, C. ;
Dangoumau, A. ;
Marouillat, S. ;
Brulard, C. ;
Chami, A. ;
Hergesheimer, R. ;
Corcia, P. ;
Blasco, H. ;
Andres, C. R. ;
Vourc'h, P. .
MOLECULAR NEUROBIOLOGY, 2018, 55 (08) :6480-6499
[92]  
Mesnil G., 2012, P ICML WORKSHOP UNSU, P97
[93]   The prediction of drug-glucuronidation parameters in humans: UDP-glucuronosyltransferase enzyme-selective substrate and inhibitor probes for reaction phenotyping and in vitro-in vivo extrapolation of drug clearance and drug-drug interaction potential [J].
Miners, John O. ;
Mackenzie, Peter I. ;
Knights, Kathleen M. .
DRUG METABOLISM REVIEWS, 2010, 42 (01) :196-208
[94]  
Mohammadzadeh-Vardin T, 2024, PLoS ONE, V19
[95]   Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System [J].
Moore, Jasmine A. ;
Tuladhar, Anup ;
Ismail, Zahinoor ;
Mouches, Pauline ;
Wilms, Matthias ;
Forkert, Nils D. .
NEUROINFORMATICS, 2023, 21 (01) :45-55
[96]   Alzheimer's disease: A lesson from mitochondrial dysfunction [J].
Moreira, Paula I. ;
Santos, Maria S. ;
Oliveira, Catarina R. .
ANTIOXIDANTS & REDOX SIGNALING, 2007, 9 (10) :1621-1630
[97]   Critical assessment of methods of protein structure prediction: Progress and new directions in round XI [J].
Moult, John ;
Fidelis, Krzysztof ;
Kryshtafovych, Andriy ;
Schwede, Torsten ;
Tramontano, Anna .
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2016, 84 :4-14
[98]   Structural basis for the autoinhibition of c-Abl tyrosine kinase [J].
Nagar, B ;
Hantschel, O ;
Young, MA ;
Scheffzek, K ;
Veach, D ;
Bornmann, V ;
Clarkson, B ;
Superti-Furga, G ;
Kuriyan, J .
CELL, 2003, 112 (06) :859-871
[99]   A deep learning approach for Parkinson's disease diagnosis from EEG signals [J].
Oh, Shu Lih ;
Hagiwara, Yuki ;
Raghavendra, U. ;
Yuvaraj, Rajamanickam ;
Arunkumar, N. ;
Murugappan, M. ;
Acharya, U. Rajendra .
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (15) :10927-10933
[100]  
Ohsfeldt Robert L, 2010, J Med Econ, V13, P428, DOI 10.3111/13696998.2010.499758