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.

该项目的目的是开发一个化学工程分子自动设计的平台。设计分子归结为识别优化应用特定特性（例如溶解度或点火延迟）的分子。该平台通过将分子表示为原子作为节点和化学键作为边缘的分子图，将分子建模与基于图的机器学习方法联系起来。该平台具有三个组件：第一个组件是用于对分子和化学混合物的物理化学特性的自动化和化学解释性预测的图形神经网络工具。第二个组件涉及使用基于图的生成机器学习的分子产生。第三个成分结合了具有所需特性的分子设计的前两个结果，这取决于新分子的产生以及其物理化学特性的预测。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，反应剂和催化剂来提高和加速化学过程的改进。该平台将在SPP 2331的GIT环境中作为开源，可用于研究人员使用，调整和积极扩展。为此，将为平台的各个组件创建化学工程的文档，教程和案例研究。同时，该平台将在SPP 2331中的其他项目中启用数据驱动的分子建模，并将通过这些应用程序用例来扩展。