脚手架GVAE:通过基于多视图图神经网络的变异自编码器生成脚手架和跳跃药物分子
Chao Hu1,2, Song Li3,1, Chenxing Yang1
1Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China.
脚手架GVAE是一种新的深度学习模型,使用脚手架跳跃生成新的药物分子. 这种方法探索了新的化学空间并创造了独特的化合物,有助于发现帕金森氏症等疾病的药物.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 深度学习正在彻底改变药物设计,特别是在分子生成方面.
- 现有的方法往往缺乏明确的脚手架跳跃策略.
- 脚手架跳跃对于产生具有改进性质的新型分子结构至关重要.
研究的目的:
- 引入ScaffoldGVAE,这是一个用于脚手架生成和跳跃的深度学习模型.
- 为了解决目前基于深度学习的分子生成的局限性.
- 通过创新的分子设计,促进新药候选药物的发现.
主要方法:
- 开发了ScaffoldGVAE,这是一个利用多视图图形神经网络的变化自编码器.
- 集成的节点中心和边缘中心消息传递,侧链嵌入和高斯混合分布.
- 通过使用一般和脚手架跳跃生成模型指标进行了全面的评估.
主要成果:
- 脚手架GVAE有效地探索未见的化学空间,产生与已知的化合物不同的新分子.
- 使用 GraphDTA,LeDock 和 MM/GBSA 验证了悬架跳跃分子.
- 证明成功生成LRRK2抑制剂用于帕金森病治疗.
结论:
- 脚手架GVAE对于脚手架生成和跳跃药物发现是有效的.
- 该模型产生新型化合物的能力有助于探索新的治疗途径.
- 这种方法有可能开发针对各种蛋白质点和疾病的新药.
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