Presentation of our Kubeflow pipelines and comparison to the Cebule API (Python SDK) for building powerful prediction pipelines.

We present a versatile graph neural network (GNN) model to predict various properties such as the HOMO-LUMO gap as well as a tutorial on training and fine-tuning custom GNN models via Cebule and/or Kubeflow.

High-throughput screening of COSMO data for thermodynamic properties.

Fine-tuning a ML Model for HOMO-LUMO gap predictions; an important property to map to a pre-liminary toxicity assessment for a wide range of chemical application domains for human and environmental safety assesments.

The 'Quantum Computing for Chemistry' course will onboard you to the exciting world of quantum computing and has a focus on quantum chemistry. Work on the course material with the high performance computing capabilities of the MQS Dashboard development environment

Develop on top of the MQS tools stack

Toxicity predictions with HOMO-LUMO gap data applied to polychlorinated biphenyls.

Combining the Search Field and Search API for the high-throughput screening of molecules.

Optimized search and molecules data sheet.

High-throughput screening of quantum chemical properties of molecules.