GPT4Kinase:使用大型语言模型高精度预测抑制剂-激酶结合亲缘关系
Kaifeng Liu1, Xiangyu Yu1, Huizi Cui1
1Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, Edmond H. Fischer Signal Transduction Laboratory, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun 130012, China.
International journal of biological macromolecules
|November 1, 2024
概括
像GPT-4这样的大型语言模型 (LLM) 准确地预测了抑制剂-激酶结合亲和力,优于传统方法. 这一突破有助于药物发现和对基因激活蛋白激酶 (MAPK) 途径的理解.
科学领域:
- 生物化学和分子生物学
- 计算生物学和化学信息学
- 药理学和药物发现
背景情况:
- 激酶抑制剂对于生物研究和医学至关重要.
- 激酶是细胞过程中的关键酶,特别是基激活蛋白激酶 (MAPK) 途径.
- 准确预测抑制剂-激酶结合亲和力对于治疗开发至关重要.
研究的目的:
- 利用大型语言模型 (LLM),特别是GPT-4,来预测MAPK通路中的抑制剂和激酶之间的结合亲和力.
- 为了比较LLM的表现与既定的计算方法.
- 识别有助于结合亲和力的分子特征,并通过实验验证它们.
主要方法:
- 利用GPT-4来预测Raf蛋白激酶 (RAF),基因激活蛋白激酶激酶 (MEK) 和细胞外信号调节激酶 (ERK) 的抑制剂-激酶结合亲和力.
- 将GPT-4的预测准确度与Autodock Vina,BatchDTA和KIPP进行了比较.
- 采用GPT-4来分析影响结合亲和力的分子特征和功能组,随后进行分子对接验证.
- 在6个额外的激酶和200多个激酶的数据集中评估了模型的概括性.
主要成果:
- 在RAF结合亲和度方面,GPT-4的准确度达到87.31%,在全面的MAPK路径预测方面达到77.00%.
- GPT-4显著超过了现有的方法,如Autodock Vina (21.21%),BatchDTA (52.00%) 和KIPP (59.60%).
- 该模型表现出强大的通用性,在其他六种激酶上达到83.78%的准确性,在200多种激酶的数据集上达到66.20%的准确性.
结论:
- LLM,特别是GPT-4,在预测分子结合亲和力方面取得了重大进展.
- 该研究强调了LLM在加速药物发现和治疗开发方面的潜力.
- GPT-4识别关键分子特征的能力为抑制剂-激酶相互作用提供了新的见解.
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