GPepT:一种包含非正规氨基酸的基础语言模型
Yuna Oikawa1, Takanori Uzawa2,3, Francois Berenger1
1Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8561, Japan.
ACS medicinal chemistry letters
|August 20, 2025
概括
这项研究介绍了GPepT,一种人工智能语言模型,使用非正规氨基酸生成多种模仿剂. 一个生成的分子显示出有前途的抗菌活性,推动了人工智能驱动的药物发现.
科学领域:
- 计算化学
- 药物发现中的人工智能
- 类科学
背景情况:
- 目前用于生成的语言模型受限于20种正规氨基酸,限制了分子多样性.
- 治疗性的开发需要探索超越标准氨基酸序列的新化学空间.
研究的目的:
- 开发一种能够产生具有增强分子多样性的体模拟物语言模型.
- 将非正规的氨基酸和终端修改纳入人工智能生成的结构.
- 证明模型在设计功能性,如抗菌剂的实用性.
主要方法:
- 在ChemBL数据库中创建了超过17,000个非正规元素的大量词汇.
- 开发并预训练了一种名为GPepT的新型语言模型,用于代.
- 精心调整的GPepT用于设计抗菌的具体任务.
主要成果:
- 与现有方法相比,GPepT语言模型在生成的分子结构和化学性质中显示出更好的多样性.
- 实验验证证了GPepT设计的模剂具有显著的抗菌活性.
- 成功展示了人工智能在发现新型治疗中的实际应用.
结论:
- 通过结合非正规元素,GPepT显著扩大了人工智能驱动的生成的范围.
- 该模型代表了在创建多样化和潜在的功能性模剂方面取得的成功.
- 这项工作突显了人工智能在加速新抗菌剂和其他治疗方法的发现方面的潜力.
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