用于预测药物向相互作用的蛋白质语言模型:新的方法,新兴的方法和未来的方向
Atabey Ünlü1, Erva Ulusoy1, Melih Gökay Yiğit2
1Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University, 06800, Ankara, Türkiye; Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University, 06800, Ankara, Türkiye.
蛋白语言模型 (pLMs) 通过预测药物向相互作用 (DTI) 来加速药物发现. 整合多样化的数据提高了准确性,尽管在改进这些深度学习方法以获得更好的生物洞察力方面仍然存在挑战.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 人工智能的人工智能
背景情况:
- 药物候选药物的识别是制药开发中的一个复杂的挑战.
- 深度学习,特别是蛋白质语言模型 (pLMs),在加速药物发现方面表现有前途.
- pLM 嵌入蛋白质特性用于预测任务,有助于理解分子相互作用.
研究的目的:
- 审查蛋白质语言模型 (pLMs) 在预测药物向相互作用 (DTI) 的应用.
- 探索使用pLMs进行DTI预测的方法,用于小分子和基于蛋白质的治疗方法.
- 突出异质数据集成在提高DTI预测准确性方面的重要性.
主要方法:
- 对应用pLM用于DTI预测的各种方法的审查.
- 分析端到端的学习模型和方法,使用预先训练有素的基础PLM.
- 检查异质数据集成,包括蛋白质结构和知识图.
主要成果:
- pLM为药物开发中的DTI预测提供了强大的工具.
- 多种数据源的整合大大提高了DTI预测的准确性.
- 目前在DTI预测方面的挑战主要与数据限制和算法约束有关.
结论:
- 进一步的研究应侧重于多式联络学习,并纳入动态交互数据.
- 需要新的深度学习架构来完善蛋白质表示和理解生物背景.
- 通过改善蛋白质表示和生物学理解来推进DTI预测对于药物开发至关重要.
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