SAFE:Synthetically Accessible Fragment Space Extensions by Machine Learning-Based Approaches

该项目的总体目标是创建预测模型，使其能够扩大可合成的化学碎片空间。当今药物发现、材料设计以及合成化学中出现的一个关键问题是如何以合理的努力精确绘制可合成的有机化合物的空间。大型制药公司以及几家大型化合物供应商通过定义可合成的化学碎片空间解决了这一问题。由于片段空间是反应模式驱动的，因此扩展问题可以分解为预测与某个反应相容的构建块以及在反应中彼此相容的构建块。在拟议的项目中，将开发一个化学信息学框架，用于从碎片空间提取关于反应方案和反应物的数据。此外，还将为反应预测中的应用设计特定问题的分子描述符。基于这些数据和表示，将期望为选定的反应方案识别允许的反应物。为此，将使用碎片空间和文献中的数据来评估和训练最先进的机器学习模型。从随机森林、支持向量机、梯度增强到神经网络，各种技术都将接受测试，以测试它们从训练数据中进行泛化的能力。为了有针对性地提高预测性能，将主动学习策略与筛选技术相结合，生成大量的实验数据。为了实现这一目标，将开发和评估新的卷积筛选技术。模型的预测能力将不断得到实验验证，以确保空间扩展的可综合性。将开发用于将被识别为具有生产性的反应转移到碎片空间的软件工具，以及用于将具有最大相容性的反应物分组的优化方案。在项目的最后阶段，将进行工作流程部分自动化的试验。

The overall aim of the project is the creation of predictive models enabling the extension of synthetically accessible chemical fragment spaces. A key question arising today in drug discovery, materials design and also synthetic chemistry is how to precisely map the space of synthetically accessible organic compounds with reasonable efforts. Large pharmaceutical companies as well as several large compound vendors addressed this question with the definition of synthetically accessible chemical fragment spaces. Since fragment spaces are reaction-pattern driven, the extension problem can be broken down to the prediction of building blocks compatible with a certain reaction and compatible with each other in a reaction. In the proposed project a chemoinformatics framework will be developed for the extraction of data on reaction schemes and reactants from fragment spaces. In addition, problem-specific molecular descriptors will be designed for the application in reaction predictions. Based on these data and representations, the identification of tolerated reactants will be aspired for selected reaction schemes. For this purpose, state-of-the-art machine learning models will be evaluated and trained with data from the fragment spaces and the literature. A large variety of techniques from random forests, support vector machines, gradient boosting, to neural networks will be tested for their ability to generalize from the training data. For the targeted improvement of the prediction performance, an active learning strategy will be combined with screening techniques to generate large amounts of experimental data. To achieve this goal, new convolutional screening techniques will be developed and evaluated. The predictive power of the model will be continuously validated experimentally to ensure the synthesizability of the spatial extensions. Software tools for transferring reactions identified as productive into fragment space, and an optimization scheme for grouping reactants with maximum compatibility will be developed. In the final phase of the project, trials for the partial automation of the workflow will be performed.