ZBH : Universität Hamburg

Aktuelles

Foto: Louis Bellmann

21. Januar 2022|AMD News

New Paper: Comparison of Combinatorial Fragment Spaces and Its Application to Ultralarge Make-on-Demand Compound Catalogs

Comparison of Ultralage Make-on-demand compound catalogs for the first time

Calculating the compound overlap of two or more chemical libraries is a common task for various applications. Until now this assessment was not possible for libraries exceeding several billions of compounds in size. We are happy to announce the...

Foto: Figure: The human kinome trees in this figure were created with KinMap (http://www.kinhub.org/kinmap/)

15. September 2021|AMD News

New Paper: Searching Geometric Patterns in Protein Binding Sites and Their Application to Data Mining in Protein Kinase Structures

Protein kinase research encounters several challenges, the most serious one being the improvement of inhibitor selectivity through compound optimization and covalent inhibitor design. In our recent publication, we face these challenges by applying data mining in the Protein Data Bank through the tool GeoMine. It is an...

Foto: Robert Schmidt

9. September 2021|AMD News

New Paper: Maximum Common Substructure Searching in Combinatorial Make-on-Demand Compound Spaces

Maximum Common Substructure (MCS) Searches are a common problem in Cheminformatics.
For the first time, we (AMD group, Prof Rarey) provide a tool efficiently calculating the MCS between a given query and compounds in non-enumerated fragment spaces, like commercial make-on-demand compound libraries. Today, more than 30...

23. August 2021|ZBH News

New Paper: DeepGRP: engineering a software tool for predicting genomic repetitive elements using Recurrent Neural Networks with attention

Repetitive elements contribute up to 50% of eukaryotic genomes.
So identifying and classifying repeats is an important step in genome
annotation. In collaboration with Fabian Hausmann (Inst. for Medical Systems
Biology, University Medical...

27. April 2021|New Paper

New Paper: Quasi-Newton Numerical Optimizer with Knowledge-Driven Step-Length Control on Input Parameter Space (LSL-BFGS)

For a better numerical optimization of molecular systems, we (AMD group, Prof. Rarey) developed a variant of the BFGS algorithm enabling *the inclusion of arbitrary domain knowledge into the step-length selection*. The new method has...