使用人工智能方法预测阿普坦酶亲和力.
Arezoo Fallah1, Seyed Asghar Havaei2, Hamid Sedighian3
1Department of Bacteriology and Virology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Journal of materials chemistry. B
|August 19, 2024
概括
通过人工智能 (AI) 和计算方法,aptamer发现得到了增强. 这些方法加快了对体的识别,这些体在各种应用中对分子向至关重要.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 胺是聚核酸分子,可以结合特定的点,类似于抗体.
- 通过SELEX进行传统的受体选择往往耗时且具有挑战性.
- 计算方法对于aptamer设计和预测越来越重要.
研究的目的:
- 审查人工智能和计算策略的进展,以预测aptamer结合能力.
- 探索机器学习,深度学习和基于结构的方法用于aptamer发现的整合.
主要方法:
- 通过指数式丰富 (SELEX) 的联体的系统演化及其局限性.
- 对RNA和DNA二次和3D结构预测的基于结构的计算方法.
- 分子对接和分子动力学模拟用于aptamer-target相互作用.
- 应用人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 模型进行约束性预测.
主要成果:
- 人工智能和DL模型在准确预测aptamer-target结合性质方面表现有前途.
- 计算方法,包括基于结构的设计和模拟,有助于aptamer选择.
- 多种不同的计算策略的整合可以克服实验性aptamer识别的局限性.
结论:
- 人工智能驱动的管道提供了一个精确可靠的方法来预测aptamer结合.
- 先进的计算技术对于高效和有效的aptamer发现和开发至关重要.
- 未来的研究应该专注于改进人工智能模型,并将它们与实验验证进行整合,以进行强大的受体选择.
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