Chemoinformatics & Chemical Biology group has its main know-how in development of computational tools for drug discovery, including Virtual Computational Chemistry Laboratory (VCCLAB) http://www.vcclab.org and On-line CHEmical Modeling Environment (OCHEM) http://ochem.eu in close collaboration with its spin-off company BIGCHEM GmbH.  We organized historically largest 28th International Conference on Artificial Neural Networks  (ICANN2019), which was attended by 500 participants and produced five volumes of proceedings.

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.) 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
• Compartmental and non-compartmental approaches in pharmacokinetic (PK) modeling
•  Linear and non-linear approaches in Pharmakodynamic (PD) modeling
• 3D Pharmacophore modeling
• Metabolite PK/PD modeling and estimation of first-pass contributions
• Chemoinformatics software development