GEP-DNN4Mol:基于深度神经网络和基因表达编程的自动化学分子设计
Wen Zheng1, Zhongji Li1, Yuanyuan Chen1
1Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001 China.
Health information science and systems
|March 27, 2025
概括
我们开发了GEP-DNN4Mol,这是一种用于分子设计的新方法,可以产生具有所需性质的多种分子. 这种方法增强了化学勘探,在有效性,新性和多样性方面超过了现有的方法.
科学领域:
- 计算化学计算化学
- 化学信息学 化学信息学
- 药物发现 药物发现 药物发现
背景情况:
- 分子反向设计对于发现新型化合物至关重要.
- 目前的方法往往缺乏足够多样化的生成分子.
- 高效地探索广的化学空间仍然是一个挑战.
研究的目的:
- 引入GEP-DNN4Mol,一种产生具有特定性质的多种分子的方法.
- 改进分子设计中化学空间的探索.
- 为了解决现有的分子生成技术中发现的多样性的局限性.
主要方法:
- 利用基因表达编程 (GEP) 作为分子结构的核心生成器.
- 采用深度神经网络 (DNN) 作为评估器,以指导基于分子特征的GEP更新.
- 整合SMILES和SELFIES分子表示,以提高兼容性和表达力.
主要成果:
- 与最先进的方法相比,GEP-DNN4Mol表现出优越的性能.
- 在生成的分子中实现了更高的有效性,新性和多样性.
- 在探索大型化学空间方面展示了提高效率.
结论:
- GEP-DNN4Mol为分子反向设计提供了一种强大的新方法.
- 该方法有效地平衡了分子性质优化与多样性.
- 这项工作推进了计算分子设计和发现领域.
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