CSearch:通过虚拟合成和全球优化进行化学空间搜索
Hakjean Kim1, Seongok Ryu2, Nuri Jung1
1Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea.
Journal of cheminformatics
|December 5, 2024
概括
计算分子设计现在有效地使用图形神经网络 (GNN) 来生成新药候选物,用于属性预测. 这种方法显著降低了计算成本,产生了强效和可合成的分子.
科学领域:
- 药物的发现和开发.
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
背景情况:
- 分子设计涉及生成分子并预测它们的特性.
- 虚拟选和图书馆选是常见的,但计算密集的方法.
研究的目的:
- 开发一种有效的分子生成方法,使用虚拟合成和全球优化.
- 为特定的目标功能优化化合物,例如结合亲和力.
主要方法:
- 使用预训练的图形神经网络 (GNN) 来估计四个目标受体的对接能量.
- 采用全球优化算法与基于片段的虚拟合成 (CSearch方法).
主要成果:
- 与虚拟选相比,生成了高度优化的化合物,计算精力比虚拟选少300-400倍.
- 实现了与已知的结合剂相似的合成能力和多样性,具有高强度和新性.
- 生产了类似药物的结合剂,具有类似于已知的抑制剂的预测结合作用.
结论:
- 该CSearch方法有效地探索化学空间寻找类似药物的分子.
- 生成的化合物是针对目标功能的优化,高效,可合成,多样化和新的.
- 在产生具有有利性质的类似药物的结合剂方面表现出有效性.
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