| Information Field | Provided Information |
|---|---|
| Laboratory /Department /Institute /Centre Name | Faculty of Science and Letter, Chemistry Department, Moleculer Modelling and Machine Learning Laboratory |
| Brief description of the Centre/Research Group (Please provide a brief description of your research group or organisational unit, including research infrastructure, laboratory facilities, team size and industry collaborations.) | Our research group focuses on the application of machine learning and computational modelling for chemical and materials discovery. We develop data-driven approaches for drug design, molecular property prediction, and materials optimization. Our work integrates cheminformatics, molecular simulations, and artificial intelligence, including active learning and deep learning methods. The group has experience with molecular dynamics simulations, cheminformatics pipelines, and predictive modelling, and collaborates with interdisciplinary partners in computational and experimental domains. |
| Research Area (Please provide 5–10 keywords that best describe your research areas and expertise.) | Drug discovery, molecular modelling Machine Learning, cheminformatics, molecular dynamics, molecular docking |
| Please list your relevant previous projects, indicating the programme, project name, year and role. | Discovery of molecules targeting RNA methylation enzymes using molecular docking, molecular dynamics, and active machine learning Programme: TÜBİTAK 3501 Role: Principal Investigator Discovery of selective molecules for HDAC isoforms using machine learning methods Programme: Scientific Research Project (Higher Education Institutions) Period: 31/05/2023 – 07/12/2023 Role: Principal Investigator Discovery of HDAC6-selective inhibitors using deep learning approaches Programme: Scientific Research Project (Higher Education Institutions) Period: 01/03/2021 – 13/02/2022 Role: Principal Investigator In silico optimization, synthesis, and biological evaluation of novel BCL-2 inhibitors Programme: TÜSEB Period: 07/06/2020 – 07/06/2023 Role: Researcher Development of diagnostics, drug formulations, and vaccines against HIV, HPV, and Influenza Programme: TÜBİTAK 1004 Period: 28/03/2020 – 30/09/2022 Role: Researcher |
| Proposed Project Idea or Area of Interest (If applicable, please briefly outline a concept idea, the problem you aim to address, or your specific area of interest.) | Development of active learning-driven platforms for efficient discovery of bioactive molecules and functional materials. The project aims to integrate machine learning, molecular simulations, and experimental validation to accelerate the identification of high-performance compounds while minimizing data requirements. |
| Potential Contributions to Projects | ☑ Scientific / technical research ☑ Methodology development ☑ Modelling / simulation ☑ Data analysis / artificial intelligence ☐ Experimental studies / testing ☐ Pilot / demonstration activities ☐ Socio-economic analysis / policy contribution ☑ Education, dissemination and impact activities ☐ Other (please specify): |
| Preferred Role in Consortia | ☐ Coordinator ☑ Partner |
| Programmes and Calls of Interest (Please indicate the programme(s) you are interested in and, if possible, specify the call identifier and/or title.) | ☑ Horizon Europe Clusters ☐ Erasmus+ ☑ Digital Europe ☐ EIT (HEI / KIC, etc.) ☑ MSCA ☑ ERC ☐ Other (please specify): |
| Additional Notes (Please provide any additional information or specific remarks you would like to share with the International Projects Office.) | Open to interdisciplinary collaborations combining computational and experimental approaches, particularly in AI-driven drug discovery and materials design. Interested in both academic and industry partnerships. |
