人工智能对神经精神疾病药物开发的影响。

IF 4.9 3区 生物学 Q1 BIOLOGY
EXCLI Journal Pub Date : 2025-07-03 eCollection Date: 2025-01-01 DOI:10.17179/excli2025-8378
Vickram Agaram Sundaram, Bharath Saravanan, Bhavani Sowndharya Balamurugan, Mathan Muthu Chinnakannu Marimuthu, Kavita Munjal, Hitesh Chopra
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引用次数: 0

摘要

人工智能(AI)和机器学习(ML)正在改变药物发现,特别是在传统药物研究存在重大障碍的神经精神疾病方面。本文着眼于人工智能如何帮助推进神经精神药物的开发,重点是早期研究、药物设计和临床诊断。本文综述了人工智能在理解血脑屏障及其与中枢神经系统的联系方面的贡献,这是神经精神治疗药物疗效的一个重要方面。人工智能促进了从头药物设计,使用预测算法和深度学习模型,加速了新药物分子的发现。人工智能被用于脑成像和诊断,提高了识别神经精神疾病的准确性。血脑屏障通透性预测是人工智能在药物发现中最重要的应用之一,因为它改善了中枢神经系统活性药物的选择。此外,人工智能正在改变神经发育障碍的治疗技术,并通过数据驱动的方法协助发现新的抗抑郁药物。尽管取得了这些成就,但人工智能驱动的药物发现仍然存在一些限制,如数据偏差、监管障碍和伦理问题。克服这些限制对于释放人工智能在神经精神病学研究中的全部潜力至关重要。本文总结了未来的几种可能性和机会,例如使用复杂的神经网络模型和多模态数据融合技术将人工智能纳入个性化医疗。这可能会通过微调人工智能方法来增加某些情况下的治疗选择。本文提出了人工智能作为影响神经精神药物开发的高度变革性工具的观点,以及一个有可能影响现代药理学思想的新兴领域。另见图解摘要(图1)。1).
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The impact of artificial intelligence on drug discovery for neuropsychiatric disorders.

The impact of artificial intelligence on drug discovery for neuropsychiatric disorders.

The impact of artificial intelligence on drug discovery for neuropsychiatric disorders.

The impact of artificial intelligence on drug discovery for neuropsychiatric disorders.

Artificial intelligence (AI) and machine learning (ML) are transforming medication discovery, particularly in neuropsychiatric illnesses, where traditional drug research presents major obstacles. This paper looks at how artificial intelligence might help advance neuropsychiatric medication development, with an emphasis on early-stage research, drug design, and clinical diagnostics. This review discusses AI's contribution to understanding the blood-brain barrier and its link with the central nervous system, which is an important aspect of medication efficacy in neuropsychiatric treatments. AI-facilitated de novo drug design, using predictive algorithms and deep learning models, speeds up the discovery of new medicinal molecules. AI is employed in brain imaging and diagnosis, boosting the accuracy with which neuropsychiatric diseases are identified. BBB permeability prediction is one of the most important uses of AI in drug discovery, as it improves the selection of CNS-active drugs. Additionally, AI is transforming treatment techniques for neurodevelopmental disorders and assisting in the discovery of novel antidepressant medications through data-driven methodologies. Despite these accomplishments, AI-driven drug discovery still has several constraints, such as data biases, regulatory barriers, and ethical issues. Overcoming these restrictions will be critical to unlocking AI's full potential in neuropsychiatric research. This paper concludes with several future possibilities and opportunities, such as incorporating AI into personalized medicine using sophisticated neural network models and multimodal data fusion techniques. This might increase treatment choices for certain conditions by fine-tuning AI approaches. This paper presents a perspective on AI as a highly transformative instrument for influencing neuropsychiatric drug development, as well as an emerging field that has the potential to impact the modern idea of pharmacology. See also the graphical abstract(Fig. 1).

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来源期刊
EXCLI Journal
EXCLI Journal BIOLOGY-
CiteScore
8.00
自引率
2.20%
发文量
65
审稿时长
6-12 weeks
期刊介绍: EXCLI Journal publishes original research reports, authoritative reviews and case reports of experimental and clinical sciences. The journal is particularly keen to keep a broad view of science and technology, and therefore welcomes papers which bridge disciplines and may not suit the narrow specialism of other journals. Although the general emphasis is on biological sciences, studies from the following fields are explicitly encouraged (alphabetical order): aging research, behavioral sciences, biochemistry, cell biology, chemistry including analytical chemistry, clinical and preclinical studies, drug development, environmental health, ergonomics, forensic medicine, genetics, hepatology and gastroenterology, immunology, neurosciences, occupational medicine, oncology and cancer research, pharmacology, proteomics, psychiatric research, psychology, systems biology, toxicology
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