CProMG:可控制的以蛋白质为导向的分子生成,具有所需的结合亲和力和类似药物的特性
Jia-Ning Li1, Guang Yang1, Peng-Cheng Zhao1
1School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
Bioinformatics (Oxford, England)
|June 30, 2023
概括
我们开发了CProMG,这是一种深度学习框架,用于生成与特定蛋白质结合的新型分子,具有高度亲和力和所需的类似药物的特性. 这种可控制的以蛋白质为导向的分子生成进步了新的药物设计.
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:
- 深度学习加速了对新分子设计的化学空间的探索.
- 产生具有特定蛋白质结合 afinities 和药物样性质的分子仍然是一个挑战.
研究的目的:
- 引入CProMG,这是可控制的蛋白质导向分子生成的新框架.
- 通过融合分层蛋白质视图来增强蛋白质结合口袋表示.
- 为了实现具有受控结合亲和力和药物样性质的分子的自回归生成.
主要方法:
- 开发了CProMG,具有3D蛋白质嵌入模块,双视图蛋白质编码器,分子嵌入模块和类似药物的分子解码器.
- 融合分层蛋白质视图,将氨基酸残留与原子关联起来,以增强结合口袋表示.
- 共同嵌入的分子序列,类似药物的特性,以及可控制生成的蛋白质结合亲和力.
主要成果:
- 与最先进的深度生成方法相比,CProMG表现出更高的性能.
- 通过逐步控制结合亲和力和类似药物的特性,证实了框架的有效性.
- 废除研究证实了分层蛋白质视图,拉普拉斯位置编码和属性控制的贡献.
结论:
- CProMG提供了一种新且有效的方法,用于可控制的蛋白质导向分子生成.
- 该框架通过捕捉关键的蛋白质分子相互作用来增强de novo分子设计.
- 预计这项工作将大大促进药物发现的进步.
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