1st International Conference on Chemo and BioInformatics, ICCBIKG  2021, (2-8)

AUTHOR(S) / АУТОР(И): Bakhtiyor Rasulev


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DOI: 10.46793/ICCBI21.002R


For the last two decades, breakthrough research has been going on in all aspects of materials science at accelerated pace. New materials of unprecedented functionality and performance are being developed and characterized. Moreover, the new materials with improved functionality are in high demand in the marketplace and this need increases in an exponential way for the new materials of desired functionality and performance.

Here we show the application of combined computational and cheminformatics methods in various materials properties prediction, including organometallic materials, polymeric materials and nanomaterials. Since most of the materials are complex entities from a chemical point of view, the investigation of them requires an interdisciplinary approach, involving multiple aspects ranging from physics and chemistry to biology and informatics. In this report we show how the combination of computational chemistry, available experimental data, machine learning and cheminformatics approaches can help in materials research and properties assessment, such as physico-chemical properties, toxicity, and biological activity. We discuss here a few case studies where data-driven models developed to reveal the relationships between the physicochemical properties, biological activity and structural characteristics, by application quantum chemical, protein-ligand docking, cheminformatics approaches and developed nanodescriptors.


materials, nanomaterials, polymeric materials, organometallic materials, cheminformatics, machine learning, computational chemistry


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