The MQS platform allows to apply end to end pipelines to high-throughput screen molecules for retrieving highly valuable data from in-silico and laboratory information sources. The available tools stack encompasses the following:
- User interface (UI) to search for molecules with search queries supporting operators and wildcards to refine a search of molecular classes [1].
- An extensive and continuously updated database of over 200000 million molecular structures and relevant quantum chemistry information, such as geometry optimized xyz-coordinates, HOMO-LUMO gap data, frequencies and vibration symmetries [2]. The MQS application programming interface (MQS-API) and software development kit (MQSDK) to allow the retrieval of data and executing ab-initio quantum chemistry models for high-throughput screening studies [3].
- Quantum computing course for integrating QPU based algorithms with ab-initio quantum chemistry models and high peformance infrastructure availability [4].
- A machine learning environment to connect individual steps in the pipeline as containers to execute highly sophisticated studies [5].
- A laboratory connection interface to apply closed-loop optimization of the design of experiments with automated laboratories.
The platform will be showcased by utilizing the UI, the integrated JupyterLab and Kubeflow environment in the MQS Dashboard.