Being a computational research lab, we develop an increasingly diverse portfolio of software tools. On this page, you will find a selection of our most popular open source projects. For an exhaustive list, make sure to visit our GitHub organization!


TeachOpenCADD is a teaching platform offering tutorials on central topics in cheminformatics and structural bioinformatics. The tutorials contain theoretical background and practical implementations using open source data and software. Implementations are available in two formats: Python-based Jupyter notebooks and GUI-based KNIME workflows.

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In this project, we aim to combine structure-enabled machine learning and alchemical free energy calculations to develop a predictive quantitative model to rapidly assess kinase inhibitor affinity and selectivity, design ligands with desired selectivity profiles and assess the impact of clinical point mutations on inhibitor binding.

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The KinFragLib project allows to explore and extend the chemical space of kinase inhibitors using data-driven fragmentation and recombination, built on available structural kinome data from the KLIFS database for over 2,500 kinase complexes. The computational fragmentation method splits known non-covalent kinase inhibitors into fragments with respect to their 3D proximity to six predefined subpockets relevant for binding.

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Protein-Ligand Interaction Frequencies across Multiple Structures.

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OpenCADD is a Python library for structural cheminformatics.

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  • kissim · Subpocket-based fingerprint for structural kinase comparison - `kissim` package
  • kissim · Subpocket-based fingerprint for structural kinase comparison - `kissim` documentation
  • kissim_app · All-against all comparison of structurally covered kinases using the KiSSim fingerprint.

Kinases are important and well studied drug targets for cancer and inflammatory diseases. Due to the highly conserved structure of kinases, especially at the ATP binding site, the main challenge when developing kinase inhibitors is achieving selectivity, which requires a comprehensive understanding of kinase similarity. In our KiSSim (Kinase Structural Similarity) project, we developed a subpocket-focused kinase fingerprint to investigate kinome-wide pocket similarity.

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  • maxsmi · Data augmentation for molecular property prediction using deep learning

Deep learning requires lots of data which in the case of physico- chemical and bioactivity remains scarce. Here, we exploit that one compound can be represented by various SMILES strings as means of data augmentation and we explore several augmentation techniques. The best strategies lead to the Maxsmi models, the models that maximize the performance in SMILES augmentation. These models are trained on four data sets, including experimental solubility, lipophilicity, and bioactivity measurements, and are available for prediction on novel compounds.

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more coming …