AI-Empowered Universal Workflow for Molecular Design of Performant Photoswitches

设计具有定制性质和功能的分子是从农业和医学到材料，能源和信息的广泛应用的核心。这个设计过程传统上是一个爱迪生式的发现，其中不完整的人类理解和工作假设被用来优先考虑有限的搜索空间。由于高吞吐量的实验和基于预测器的预测质量计算，这些搜索空间可以逐渐扩展。然而，与广阔的化学空间相比，它们仍然微不足道。因此，该提案旨在利用现代人工智能（AI）方法来建立一个有目的的实际设计过程。如果有足够的数据，生成模型可以隐式地学习潜在的结构-性质关系，然后直接提出满足目标性质的创新分子设计。一个根本的挑战是这种深度学习通常需要大量的数据。为此，我们将利用迁移学习概念来减少所需的特定领域数据量，开发计算上最有效的描述符来增加合成数据的可用性，并通过采用一锅策略和自动化工作流程来生成扩展的实验化学库。可视化和可解释的人工智能分析工具将最终用于将隐式学习的结构-性质关系转换为化学可解释的知识。这不仅会增加信任和接受度，还将为已建立的人工智能框架提供重要的验证和反馈，并建立对可应用于更广泛的分子和功能空间的管理原则的可转移见解。作为一个极具挑战性，但同样有益和紧迫的设计问题，我们特别奋进设计高性能的光开关分子。优化通常相互矛盾的性能参数，如可寻址性，效率和鲁棒性，分子光开关的相应开发是一个不平凡的，迄今为止缓慢的经验过程，因此构成了一个原型应用案例，将大大受益于AI授权的分子设计。

The design of molecules with tailored properties and functions is central to a wide range of applications from agriculture and medicine to materials, energy, and information. This design process is traditionally rather an Edisonian-type discovery in which incomplete human understanding and working hypotheses are used to prioritize restricted search spaces. Thanks to high-throughput experimentation and descriptor-based predictive-quality computations these search spaces could gradually be extended. However, they are still insignificant compared to the vastness of the chemical space. This proposal therefore aims at leveraging modern artificial intelligence (AI) methodology to establish a purposeful, actual design process. Provided with sufficient amounts of data, generative models can implicitly learn the underlying structure-property relationships and then directly propose innovative molecular designs that fulfill targeted properties. A fundamental challenge is the huge amount of data generally required by such deep learning. To this end, we will exploit transfer learning concepts to reduce the amount of domain-specific data needed, develop computationally most efficient descriptors to increase the availability of synthetic data, and generate extended experimental chemical libraries by adopting one-pot strategies and automatized workflows. Visualization and explainable AI analysis tools will finally be employed to convert the implicitly learned structure-property relationships into chemically interpretable knowledge. This will not least increase trust and acceptance, will provide important validation and feedback for the established AI framework, and establish transferable insight into governing principles that can be applied across a wider space of molecules and functionalities. As a highly challenging, but equally rewarding and pressing design problem, we specifically endeavor to design high-performance photoswitchable molecules. Optimizing often contradictory performance parameters like addressability, efficiency and robustness, the corresponding development of molecular photoswitches is a non-trivial and hitherto slow empirical process that thus constitutes a prototypical application case that will heavily benefit from an AI-empowered molecular design.