用深度学习模型探索宏环化学空间,用于启发式药物设计
Feng Hu1, Xiaotong Jia1, Wenjie Liao1
1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
Communications chemistry
|October 7, 2025
概括
循环GPT是一种新的生成模型,通过增强结构性新性和数据效率来克服宏循环药物发现的局限性. 这种方法成功地确定了一种有希望的JAK2抑制剂,用于治疗多细胞血症.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 宏环化合物显示治疗潜力,但由于有限的生物活性候选物,在结构优化方面面临挑战.
- 对宏观循环的结构-活动关系的探索受到数据稀缺的阻碍.
研究的目的:
- 介绍CycleGPT,一种生成化学语言模型,以应对宏循环药物设计中的挑战.
- 提高新型和适应性的宏循环结构的生成.
- 证明CycleGPT在识别潜在候选药物的有用性.
主要方法:
- 开发了CycleGPT,使用渐进的转移学习范式,用于专门的宏观循环生成.
- 实施了概率抽样策略,以增强结构新性和领域适应性.
- 集成的CycleGPT与JAK2活动预测模型用于潜在的药物设计.
主要成果:
- 循环GPT有效地克服了宏观循环生成中的数据短缺问题.
- 生成的宏观循环表现出更好的结构新性和特定领域的适应性.
- 成功确定了一种具有高选择性的新型JAK2候选药物 (抑制17种野生类型激酶).
结论:
- 循环GPT在基于深度学习的宏循环药物设计中展示了实用的实用性.
- 鉴定到的JAK2抑制剂在体内治疗多细胞血症方面表现有前途.
- 这种生成模型有助于系统地探索宏观周期结构-活动关系.
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