对分子图形结构的等价生成框架 共同设计
Zaixi Zhang1,2, Qi Liu1,2, Chee-Kong Lee3
1Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China Hefei Anhui 230026 China qiliuql@ustc.edu.cn.
Chemical science
|August 11, 2023
概括
一个新的框架MolCode集成了2D和3D分子数据,以改进新的分子设计. 这种方法增强了有效的,多样化的分子的生成,具有药物发现和材料科学所需的特性.
科学领域:
- 计算化学的计算化学
- 机器学习 机器学习
- 药物发现 药物发现 药物发现
背景情况:
- 设计具有特定性质的分子是化学和药物发现的关键挑战.
- 现有的机器学习方法往往无法统一二维和三维分子信息,从而限制了它们的有效性.
- 需要先进的生成模型,可以学习分子设计的结构-属性关系.
研究的目的:
- 介绍MolCode,一种用于分子图形结构共同设计的新型生成框架.
- 利用3D几何信息来增强2D分子图形生成和指导3D结构预测.
- 证明框架能够设计具有理想性质和高目标亲和度的分子.
主要方法:
- 开发了MolCode,这是一个成形翻译等价变量生成框架.
- 将3D几何信息集成到2D分子图形生成中.
- 利用生成的2D图表来指导3D分子结构预测.
- 评估了 de novo分子设计,向分子发现和基于结构的药物设计任务的性能.
主要成果:
- 在生成分子图形和结构方面,MolCode实现了高有效性 (99.95%) 和独特性 (98.75%).
- 该框架成功地产生了与向蛋白具有显著亲和力的药物样分子 (61.8%的高亲和率).
- 在各种分子设计和发现任务中,MolCode的性能优于现有的方法.
结论:
- 2D拓和3D几何的整合提供了对有效分子设计至关重要的互补信息.
- MolCode为新的分子设计,材料科学和药物发现提供了一种强大而通用的方法.
- 这项研究为基于机器学习的分子表示和生成提供了新的见解.
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