Life Sciences and eHealth
Massive Biological Data Clustering, Reporting and Visualization Tools
Sequence validation in the DNA barcoding project
Combining molecular simulation and eScience technologies
Drugs typically exert their effects by binding to proteins. Methods are therefore needed to predict protein-drug binding but efficiently doing so is difficult and requires computationally demanding techniques to appropriately account for the dynamics of the binding process, especially for many pharmaceutically relevant families of enzymes or other proteins.
“Making many predictions quickly.”
Overcoming current challenges
This project aims for efficient prediction of protein-drug binding interactions by introducing and combining modelling and eScience technologies. For that purpose molecular simulation methods, algorithms, and efficient computing and data handling techniques will be combined to overcome current methodological challenges. A platform will be introduced that enables combination of such methodologies into eScience workflows for evaluating protein-drug binding in applied settings.
“Protein binding prediction for therapeutical purposes.”
Software platform for discovery and optimization
In this project methodologies will be realized as a heterogeneous and HPC driven eScience platform. For future modelling efforts, handling of large sets of calibration data is crucial. These are available via direct collaborations with academic and industry partners, who have shown strong interest in our eScience and open-source approach. The platform will enable efficient molecular simulation workflows in applied and industrial setting, e.g. in the context of drug discovery and optimization for cancer or other therapy.
Image: Drug binding to receptor protein by Sam Hertig – http://www.samhertig.ch/blog/wp-content/uploads/2015/10/m4v1m2.jpg
Sequence validation in the DNA barcoding project
Identification and prioritization of cancer-causing structural variations in patient-derived whole...
Scoring 3D protein-protein interaction models using deep learning
Fusible evolutionary deep neural network mixture learning from distributed data for robust medical...
Are you a researcher who could benefit from our eScience skills and experience? Reach out to us today and let’s explore how we can work together.
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