自然语言处理方法用于研究蛋白质-连接物相互作用
James Michels1, Ramya Bandarupalli2, Amin Ahangar Akbari2
1Department of Computer and Information Science, University of Mississippi, University, Mississippi 38677, United States.
Journal of chemical information and modeling
|February 24, 2025
概括
自然语言处理 (NLP) 通过分析蛋白质和连接体相互作用来推进药物发现. 本综述探讨了NLP方法来预测这些相互作用,突出挑战和未来的方向.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 人工智能的人工智能是人工智能.
背景情况:
- 自然语言处理 (NLP) 对于研究蛋白质和连接体结合,这是药物发现的关键过程,越来越重要.
- 调整NLP技术以了解蛋白质和连接体的"语言"可以预测关键的相互作用.
研究的目的:
- 审查NLP技术在预测蛋白质 - 配体相互作用 (PLI) 的应用.
- 讨论先进的NLP方法及其在解码药物开发中的分子相互作用中的作用.
主要方法:
- 检查NLP方法,如长期短期记忆 (LSTM) 网络,变压器和注意力机制.
- 利用各种蛋白质和连接体数据类型,通过机器学习识别相互作用模式.
主要成果:
- NLP模型可以分析复杂的分子数据,以预测潜在的蛋白质-连接体相互作用.
- 确定的挑战包括数据稀缺性,模型解释性和采样偏差.
结论:
- 提高数据质量和模型稳定性对于在PLI预测中推进机器学习至关重要.
- 合作和竞争可以加速人工智能驱动药物发现的进展.
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