通过生成性深度学习来自动设计多目标连接体
Laura Isigkeit1, Tim Hörmann2, Espen Schallmayer1
1Goethe University Frankfurt, Institute of Pharmaceutical Chemistry, 60438, Frankfurt, Germany.
Nature communications
|September 11, 2024
概括
生成型深度学习模型,特别是化学语言模型 (CLM),可以设计新的多目标药物联体. 这种方法通过同时产生对多种蛋白质活性分子来加速药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 医学中的人工智能
背景情况:
- 生成型深度学习模型,包括化学语言模型 (CLM),是数据驱动的de novo分子设计的强大工具.
- 经过像SMILES这样的分子串表示训练的CLM在设计针对特定目标的已确认活性的新型化学实体方面取得了成功.
研究的目的:
- 调查CLM的应用,用于多标配体的de novo设计,从而实现设计的多药理学.
- 利用CLM从小型微调数据集中学习的能力,将分子设计偏向于类似药物的特性和与已知的对目标对的相似性.
主要方法:
- 对特定目标对的已知配体的小数据集进行微调CLM.
- 产生新的分子设计,预测对多个蛋白质点具有活力.
- 合成和实验测试计算偏好的CLM设计.
主要成果:
- 预计产生的分子对两个标蛋白都具有活性,并从两个标的配体中纳入了来自两个标的药元素.
- 12个CLM设计的实验验证显示至少一个预期的蛋白质的调制,其强度高达两位数的纳米分子.
- 七种化合物被证实是设计的双联体,证明了成功的多目标联体生成.
结论:
- 化学语言模型对于联体的多目标de novo设计是有效的.
- 这种方法是药物发现创新的重要来源,用于开发多药物制剂.
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