Research

Main research and development topics

  • Artificial intelligence and machine learning in chemistry: design of workflows for application of artificial neural networks and machine learning algorithms in analysis and modeling complex data.
  • Quantitative and qualitative structure-property dependencies (QSAR): design and use of modern technology and software to develop QSAR models and application of these models - prediction of chemical and material properties.
  • Molecular design of biotechnological and macromolecular systems: creation and analysis of libraries of chemical compounds; virtual screening of proteins and ligands, incl. analysis of Adsorption, Distribution, Metabolism, Excretion and Toxicity profiles of chemical compounds; molecular docking; molecular dynamics.
  • Chemical synthesis and characterization of substances: modification of active molecules (in silico ↔ synthesis), absorption properties of drug substances and their candidates (pH profiles of membrane permeability).
  • Nanomaterials: structural properties of application-oriented carbon materials
  • Chemical data management and databases: Open data in chemistry and related disciplines, application of FAIR principles for chemical data and in silico models, archiving and making available prediction models, (www.QsarDB.org ).
  • Cheminformatics: application of informatics methods for chemical data mining, analysis of big and diverse/variable data in chemistry.
  • Predictive and computational toxicology: methodology and applications for environmental risk assessment of materials and chemical compounds, incl. nano-particle toxicology.
  • Quantum chemistry: research and applications to describe molecular systems in gas phase and condensed media.
  • Working environments in computer chemistry based on distributed computing and cloud technologies.