通过深度学习预测蛋白质-质结合亲和力的进展:对数据集,数据预处理技术和模型架构的全面研究
Gelany Aly Abdelkader1, Jeong-Dong Kim1,2,3
1Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, Republic of Korea.
Current drug targets
|September 25, 2024
概括
深度学习 (DL) 模型通过预测蛋白质-连接体结合亲和力 (BAP) 来加速药物发现. 这项调查分析了BAP数据集和DL方法,突出了改善药物开发的挑战和未来方向.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
背景情况:
- 药物发现是复杂而昂贵的,而化合物识别是关键阶段.
- 计算方法,特别是深度学习 (DL),越来越多地用于预测蛋白质-连接体相互作用和结合亲和力.
- 现有的研究缺乏对数据集的全面分析和最新的DL方法来预测约束亲和力 (BAP).
研究的目的:
- 为BAP提供常用数据集的全面调查,讨论其质量和局限性.
- 为了分类和分析最近的DL方法应用于BAP.
- 为BAP中DL不断发展的领域提供一个新的视角.
主要方法:
- 对BAP常用的数据集进行系统检查,包括其特征和预处理步骤.
- 审查各种DL技术,如图形神经网络,卷积神经网络和BAP的变压器.
- 广泛的文献研究,包括BAP的最新DL方法.
主要成果:
- 识别了基于DL的BAP固有的挑战,包括数据质量,模型可解释性和可解释性.
- 突出了未来的研究方向的关键考虑因素,包括DL for BAP.
- 提供了有价值的见解,以加快为BAP开发有效和可靠的DL模型的发展.
结论:
- 这项研究提供了一个全面的概述,可以显著增强未来的研究,预测蛋白质 - 配体结合亲和力.
- 改进的BAP模型可以加快化合物的识别,从而简化整体药物开发过程.
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