Over the last decade AI/ML methodologies have evolved rapidly and reached a maturity that sparked the creation of predictive modelling tools for the prediction and optimization of chemical synthesis pathways. To fully leverage these tools, it requires (1) the integration of internal experimental results, as captured in Electronic Lab Notebooks or generated by high-throughput techniques, such as automated synthesis and (2) and efficient way to share and visualize the predictions. The problem is that the communication between the individual applications supporting chemists in their workflows is much less developed; there is no common language to exchange data between most of these tools. This makes data exchange and integration unnecessarily complex. The Unified Data Model (UDM) aims at improving data exchange and integration of chemical reaction data for efficient search, visualization, analysis and predictive modeling. The UDM project is a collective effort of vendors and life science organizations to create an open, extendable, and freely available reference model and data format for the exchange of experimental information about compound synthesis and testing. The UDM project has been fully moved to open source under the MIT license this year. In our presentation, we will discuss the current status of the data exchange and integration needs and how the UDM can facilitate an easy exchange of data in a variety of reaction data workflows and provide an example of how the UDM is used to simplify the data exchange

UDM: The unified data model

ACS Fall 2021

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