CV
Curriculum Vitae
Last updated: November 2025
A full PDF CV is available on request.
Education
- PhD in Computer Science, Constructor University Bremen, Germany
2024 – present- Research group of Prof. Petr Popov
- Focus: structural bioinformatics, deep learning, generative models for drug design.
- Research group of Prof. Petr Popov
- PhD (Candidate of Sciences), Skolkovo Institute of Science and Technology (Skoltech), Russia
2020 – 2024- Computational and Data Science and Engineering (CDSE), iMolecule lab
- Thesis: deep learning for binding site identification in macromolecules.
- Computational and Data Science and Engineering (CDSE), iMolecule lab
- MSc in Applied Physics and Mathematics, Moscow Institute of Physics and Technology (MIPT), Russia
2018 – 2020- Biophysics track, Laboratory of Structural Biology of G Protein-Coupled Receptors
- GPA: 4.83 / 5.
- Biophysics track, Laboratory of Structural Biology of G Protein-Coupled Receptors
- BSc in Applied Physics and Mathematics, Moscow Institute of Physics and Technology (MIPT), Russia
2014 – 2018- Biophysics track, Department of General and Applied Physics
- GPA: 4.38 / 5.
- Biophysics track, Department of General and Applied Physics
Experience
- Researcher / Software Developer, Tetra D AG, Switzerland
2024 – present- Development of a computational platform for structure-based drug design.
- Design and implementation of hierarchical 3D latent diffusion models for molecule generation.
- Development of a computational platform for structure-based drug design.
- Researcher / PhD Student, iMolecule lab, Skoltech
2019 – 2024- Deep learning for binding site identification in proteins, nucleic acids, and complexes.
- Large-scale dataset curation, model development (BiteNet family), and case studies on diverse targets.
- Deep learning for binding site identification in proteins, nucleic acids, and complexes.
- Researcher (BSc/MSc Student), Laboratory of Structural Biology of GPCRs, MIPT
2017 – 2020- Binding-site detection on protein complexes using 3D CNNs.
- Identification of stabilizing mutations in GPCRs using machine learning.
- Binding-site detection on protein complexes using 3D CNNs.
Selected Publications
Computational methods for binding site prediction on macromolecules
Kozlovskii, I., Popov, P.
Quarterly Reviews of Biophysics, 58, e12, 2025.
10.1017/S003358352500006XApproaching Optimal pH Enzyme Prediction with Large Language Models
Zaretckii, M., Buslaev, P., Kozlovskii, I., Morozov, A., Popov, P.
ACS Synthetic Biology, 13(9), 3013–3021, 2024.
10.1021/acssynbio.4c00465Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites
Popov, P., Kalinin, R., Buslaev, P., Kozlovskii, I., Zaretckii, M., Karlov, D., … Stepanov, A.
Briefings in Bioinformatics, 25(1), bbad459, 2024.
10.1093/bib/bbad459A community effort in SARS-CoV-2 drug discovery
Schimunek, J., Seidl, P., Elez, K., Hempel, T., Le, T., Noé, F., … Kozlovskii, I., … Varnek, A.
Molecular Informatics, 43(1), e202300262, 2024.
10.1002/minf.202300262Structure-based deep learning for binding site detection in nucleic acid macromolecules
Kozlovskii, I., & Popov, P.
NAR Genomics and Bioinformatics, 3(4), lqab111, 2021.
10.1093/nargab/lqab111Protein–peptide binding site detection using 3D convolutional neural networks
Kozlovskii, I., & Popov, P.
Journal of Chemical Information and Modeling, 61(8), 3814–3823, 2021.
10.1021/acs.jcim.1c00475Spatiotemporal identification of druggable binding sites using deep learning
Kozlovskii, I., & Popov, P.
Communications Biology, 3, 618, 2020.
10.1038/s42003-020-01350-0
Research Projects
- Binding site detection with 3D CNNs (BiteNet family)
- Fast voxelization of protein atomic environments (C++/CUDA, OpenMP).
- 3D YOLO-like architectures for binding site detection on proteins, peptides, and nucleic acids.
- Large-scale PDB-derived datasets with sequence/structure/site-based de-duplication.
- Fast voxelization of protein atomic environments (C++/CUDA, OpenMP).
- Hierarchical 3D latent diffusion for molecule design
- Hierarchical pipeline combining VAE, diffusion model, and reconstruction modules for 3D molecules.
- Focus on structure-based generative design for drug discovery.
- Hierarchical pipeline combining VAE, diffusion model, and reconstruction modules for 3D molecules.
- ML for thermostabilizing mutations in GPCRs
- Structural and physicochemical descriptor pipelines for mutated positions.
- Ensemble models for classifying stabilizing vs non-stabilizing mutations.
- Structural and physicochemical descriptor pipelines for mutated positions.
Technical Skills
- Programming: Python, C++, CUDA
- Deep learning: PyTorch, TensorFlow, PyTorch Lightning
- Scientific stack: NumPy, SciPy, pandas, scikit-learn, matplotlib, seaborn, numba, polars, plotly
- Bioinformatics / cheminformatics: Biopython, MDAnalysis, RDKit, Modeller, PyRosetta, PDBFixer, parasail
- Simulation & modeling tools: GROMACS, ICM-Pro, HH-suite, MMseqs2, FoldSeek, MAFFT, BLAST, Rosetta, AlphaFold2
- Molecular visualization: PyMOL, VMD, SAMSON, py3Dmol
- Tooling & infrastructure: Git, Docker, Slurm, Conda, pyenv, Poetry, Pixi, Flask
Teaching
- Teaching Assistant, “Business Communication” course, Skoltech (2023)
- Guest lectures on machine learning topics (decision trees, ensembles, etc.) at university level.
Scholarships & Awards
- Presidential Scholarship of the Russian Federation, 2022–2023
- Copenhagen Bioinformatics Hackathon 2021 – 1st place (optimal pH prediction challenge).
- Health Data Hack 2022 – 3rd place (cancer cell detection in histopathology, CV track).
- Other hackathons and competitions are listed in the full CV.
Patents and Software
- Certificate of State Registration of a Computer Program No. RU 2023613561 – BiteNet (v1.2), 2023.
- Patent No. RU 2743316 – Method for identification of binding sites of protein complexes, 2021.
- Certificate of State Registration of a Computer Program No. RU 2020661135 – BiteNet, 2020.