Machine learning in Alzheimer's disease drug discovery and target identification
Machine learning in Alzheimer's disease drug discovery and target identification
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DOI：
10.1016/j.arr.2023.102172
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发表时间：
2023-12-29
影响因子：
13.1
通讯作者：
Tang，Yi
中科院分区：
文献类型：
--
作者：
Geng，Chaofan;Wang，ZhiBin;Tang，Yi
阿尔茨海默病（AD）是一种严重的神经退行性疾病，对老年人群构成重大威胁，目前尚无已知的治疗或减缓疾病的药物。在药物发现过程中至关重要且耗时的阶段中，疾病建模和靶标识别具有特别重要的意义。疾病建模可以更深入地理解疾病进展机制和潜在的治疗途径。另一方面，靶点识别是药物研发的基础步骤，对后续各个阶段产生深远影响，并最终决定药物研发的成功率。机器学习 (ML) 技术在目标发现领域带来了变革性突破。机器学习利用大数据集分析、多方面数据处理和复杂生物机制探索的优势，在寻求有效的 AD 治疗方面发挥了重要作用。在这篇全面的综述中，我们介绍了如何部署机器学习方法来寻求 AD 药物发现。此外，我们概述了机器学习在发现 AD 潜在干预策略和前瞻性治疗目标方面的应用。最后，我们讨论这些方法目前面临的主要挑战和局限性。我们还探索未来研究的途径，有望解决这些挑战。
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.