蛋白质语言模型的预测和治疗应用
Kairi Furui1, Koh Sakano1, Masahito Ohue1
1Department of Computer Science, School of Computing, Institute of Science Tokyo, Tokyo, Japan.
概括
蛋白质语言模型 (pLMs) 正通过利用AI对序列数据的利用来彻底改变药物发现. 本综述探讨了pLM在治疗中的应用,从预测任务到未来疾病贡献.
科学领域:
- 人工智能的人工智能
- 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:
- 蛋白质语言模型 (pLMs) 是在庞大的序列数据上训练的先进AI工具.
- 这些模型为复杂的生物问题提供了强大的表示能力.
- pLM正在改变治疗研究中的传统方法.
研究的目的:
- 提供关于PLM及其实施的全面审查.
- 探索PLM在药物发现和治疗研究中的实用性.
- 讨论目前的趋势,应用和未来的pLMs的前景.
主要方法:
- 基于架构和数据源的plm系统分类.
- 讨论整合多种生物信息 (共同进化,结构,功能) 的多式联络方法.
- 对抗体和T细胞受体的特定领域模型的概述.
主要成果:
- pLM在各种治疗应用中显示出显著的潜力.
- 关键应用包括突变效应预测,蛋白质功能预测和结构预测.
- 多模式和特定领域的模型代表了新兴趋势.
结论:
- pLMs可能会对药物发现和治疗开发产生重大影响.
- 在将pLM能力转化为实际临床应用方面仍然存在挑战.
- 未来的研究应该专注于克服这些挑战,以实现现实世界的疾病贡献.
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