周围的SMILES:结构转化为SMILES,用于过渡金属复合物的转化
Maria H Rasmussen1, Magnus Strandgaard1, Julius Seumer1
1Department of Chemistry, University of Copenhagen, Copenhagen, Denmark.
Journal of cheminformatics
|April 28, 2025
概括
我们开发了一种方法,从它们的3D结构中为过渡金属复合体 (TMC) 生成RDKit可解析的SMILES. 这使得使用标准分子表示的机器学习研究成为可能,从而创建了一个大型的TMC SMILES数据集.
科学领域:
- 计算化学是一种计算化学.
- 材料科学 是一种材料科学.
- 化学信息学 化学信息学
背景情况:
- 为过渡金属复合体 (TMCs) 生成精确的SMILES字符串是具有挑战性的,因为它们具有独特的粘合.
- 现有的方法通常需要手动验证,或缺乏广泛的适用性.
- 对于大规模的计算研究,需要一个标准化,可解析的表示.
研究的目的:
- 介绍一种用于从xyz坐标生成TMC的RDKit可解析SMILES的新方法.
- 为机器学习应用程序创建一个全面的TMC SMILES数据集.
- 评估基于SMILES的分子表示的性能,以预测TMC属性.
主要方法:
- 开发了一种将xyz坐标和充电转换为RDKit可解析的SMILES的方法.
- 使用自然债券轨道 (NBO) 分析和纠正剑桥结构数据库 (CSD) 条目生成的SMILES.
- 在一个CSD子集 (tmQMg) 上比较了三个SMILES生成策略,达到>70%的协议.
- 从SMILES创建了分子指纹和图形表示,用于机器学习.
主要成果:
- 这种新方法为TMCs产生了RDKit可解析的SMILES.
- 三种方法的比较显示,对于SMILES的生成,超过70%的协议.
- 基于SMILES的分子指纹和图表表现与基于DFT的方法进行了可比性,用于预测诸如极化性和二极子时刻等属性.
- 为单核TMCs生成了227,000个RDKit可解析的SMILES数据集.
结论:
- 开发的方法提供了一种可靠的方式来为TMCs生成可解析的SMILES.
- 基于SMILES的表示是TMC机器学习的有效和高效基线.
- 生成的数据集有助于进一步研究计算材料科学和药物发现.
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