Graph-Based Generative Machine Learning for Optimal Molecular Design

用于优化分子设计的基于图的生成机器学习

• 批准号: 466417970
• 负责人: Professor Dr. Martin Grohe
• 金额: --
• 依托单位: Informatik 7 - Lehrstuhl Logik und Theorie diskreter Systeme
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/466417970?language=en
• 关键词: Graph; Based; Generative; Machine; Learning

The objective of this project is to develop a platform for the automated design of molecules in Chemical Engineering. Designing molecules boils down to identifying molecules that optimize application-specific properties such as solubility or ignition delay. The platform connects molecular modeling with graph-based Machine Learning methods by representing molecules as molecular graphs with atoms as nodes and chemical bonds as edges. The platform has three components: The first component is a graph neural network tool for the automated and chemically interpretable prediction of physicochemical properties of molecules and chemical mixtures. The second component involves the generation of molecules using graph-based generative Machine Learning. The third component combines the results of the first two for the design of molecules with desired properties, which depends on both the generation of novel molecules as well as the prediction of their physicochemical properties. In particular, the platform will advance and accelerate the improvement of chemical processes through the automated discovery of novel working fluids, solvents, reactants, and catalysts with optimal properties. The platform will be made available as open-source in the Git-environment of the SPP 2331 and can be used, adapted, and actively extended by researchers. To this end, documentation, tutorials, and case studies from Chemical Engineering will be created for the individual components of the platform. At the same time, the platform will enable data-driven molecular modeling in other projects within the SPP 2331 and will be scaled up through these application use cases.

该项目的目标是开发一个平台，用于化学工程中分子的自动化设计。设计分子归结为识别优化应用特定特性的分子，例如溶解度或点火延迟。该平台将分子建模与基于图的机器学习方法相结合，将分子表示为分子图，原子作为节点，化学键作为边。该平台有三个组成部分：第一个组成部分是一个图形神经网络工具，用于分子和化学混合物的物理化学性质的自动化和化学可解释的预测。第二部分涉及使用基于图形的生成机器学习生成分子。第三个组成部分结合了前两个的结果，用于设计具有所需特性的分子，这取决于新分子的产生以及它们的物理化学性质的预测。特别是，该平台将通过自动发现具有最佳性能的新型工作流体，溶剂，反应物和催化剂来推进和加速化学过程的改进。该平台将在SPP 2331的Git环境中以开源形式提供，研究人员可以使用，调整和积极扩展。为此，将为平台的各个组件创建来自化学工程的文档、教程和案例研究。同时，该平台将在SPP 2331的其他项目中实现数据驱动的分子建模，并将通过这些应用用例进行扩展。