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Helmholtz Munich | © Winfried Meining

Tetko Lab

Chemoinformatics & Chemical Biology group develops computational tools for drug discovery, including Virtual Computational Chemistry Laboratory (VCCLAB) http://www.vcclab.org and On-line CHEmical Modeling Environment (OCHEM) https://ochem.eu in close collaboration with its spin-off company BIGCHEM GmbH (https://bigchem.de). 
 

Chemoinformatics & Chemical Biology group develops computational tools for drug discovery, including Virtual Computational Chemistry Laboratory (VCCLAB) http://www.vcclab.org and On-line CHEmical Modeling Environment (OCHEM) https://ochem.eu in close collaboration with its spin-off company BIGCHEM GmbH (https://bigchem.de).

About us

Our expertise covers: 

• Development of QSPR models to predict physico-chemical and biological activity of molecules

• Use of different structure representations (topological, inductive, quantum-chemical, shape signatures, pH-dependent descriptors, etc.) as well as representation learning to achieve the most accurate predictions

• Use of a large set of linear and non-linear machine learning methods to address data of different complexity and nature

• Interpretation of structure-activity relationships

• Estimation of the accuracy of predictions

• Experimental design to identify a representative set of molecules

• Analysis and interpretation of large datasets of molecules

• Chemoinformatics software development

• Prediction of chemical reactions

 

Group members

Marchela Pandelova

Varvara Voinarovska

PhD student

Paula Torren-Peraire

PhD student

Peter Hartog

PhD student

Julian Cremer

Master student

Nesma Mousma

Master student

Andrea Kopp

Our Research Topics

• Development of QSPR models to predict physico-chemical and biological activity of molecules

• Use of different structure representations (topological, inductive, quantum-chemical, shape signatures, pH-dependent descriptors, etc.) as well as representation learning to achieve the most accurate predictions

• Use of a large set of linear and non-linear machine learning methods to address data of different complexity and nature

• Interpretation of structure-activity relationships

• Estimation of the accuracy of predictions

• Experimental design to identify a representative set of molecules

• Analysis and interpretation of large datasets of molecules

• Chemoinformatics software development

• Prediction of chemical reactions

 

Publications

Read more

Tetko Lab

Contact

Dr. Igor Tetko

Research Group Leader