通过在低数据模式下进行碎片增强型生成深度学习 (Fragment-Augmented Generative Deep Learning) 来设计Nurr1抗体
Marco Ballarotto1,2, Sabine Willems1, Tanja Stiller1
1Department of Pharmacy, Ludwig-Maximilians-Universität (LMU) München, 81377 Munich, Germany.
Journal of medicinal chemistry
|May 31, 2023
概括
化学语言模型 (CLM) 现在可以设计新的药物候选者,即使数据有限. 一个微调的CLM成功地从单个模板分子中产生了强大的Nurr1激动剂.
科学领域:
- 药用化学 医学化学
- 人工智能在药物发现中的作用
- 计算化学的计算化学
背景情况:
- 生成神经网络或化学语言模型 (CLM) 越来越多地用于*de novo*分子设计.
- CLM通常需要广泛的训练数据集 (数十个模板分子) 来进行微调.
- 将CLM应用于具有稀缺已知的配体的孤儿目标是一个重大挑战.
研究的目的:
- 为了研究微调CLM的可行性,使用最小的数据来生成新的生物活性分子.
- 用单个模板分子使用片段增强的CLM方法开发新的Nurr1激动剂.
- 评估CLM在药物发现数据非常低的场景中的有效性.
主要方法:
- 微调一个使用单一强大的Nurr1激动剂作为模板的CLM.
- 在分子设计中采用碎片增强方法.
- 利用采样频率来优先考虑生成的设计.
- 评估新型Nurr1激动剂的功效和结合亲和力.
主要成果:
- 新的Nurr1激动剂被成功设计和合成.
- 排名最高的设计分子表现出纳米分子强度和结合亲和力.
- 与现有的Nurr1配体相比,产生的化合物表现出显著的结构新性.
- 该研究证实了CLM在非常低数据场景中的适用性.
结论:
- 一个微调的CLM可以有效地产生新的生物活性分子,即使只有一个模板.
- 这种方法对于发现Nurr1.1等具有挑战性的目标的线索是有价值的.
- 在数据稀缺的环境中,CLM显示了药物发现的巨大潜力.
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