CV

Structural bioinformatics and deep learning researcher working on binding-site prediction, molecular machine learning, and structure-based drug design.

Last updated: August 2026

Structural bioinformatics and deep learning researcher working on binding-site prediction, molecular machine learning, and structure-based drug design. Strongest in 3D deep learning on voxelized biomolecular structures — object-detection-style models for binding sites across proteins, peptides, nucleic acids, and ions — and in end-to-end scientific ML pipelines from structural database mining through leakage-aware benchmarking.

Current position

  • PhD student in Computer Science, Constructor University Bremen, Germany — 2024–present
    • Research group of Prof. Petr Popov
    • Hierarchical 3D generative pipeline for structure-based drug design: latent representation learning, VAE-style modeling, latent diffusion, and molecule reconstruction.

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.
  • PhD / Candidate of Sciences, Skolkovo Institute of Science and Technology (Skoltech), Russia 2020 – 2024; defended 2025
    • Computational and Data Science and Engineering (CDSE), iMolecule lab
    • Thesis: deep learning for binding site identification in macromolecules.
  • 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.
  • 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.

Selected experience

  • Software Engineer, Scientific ML Platform, Tetra D AG, Switzerland 2024 – 2025 (part-time)
    • Software components for molecular ML and structure-based drug-design workflows.
    • Data/model infrastructure: dataset preparation, training/evaluation utilities, reproducible environments.
  • Researcher / PhD Student, iMolecule lab, Skoltech 2019 – 2023
    • 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.
  • 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.

Selected publications

  • Multivalent ion binding site identification with structure-based deep learning — Kozlovskii, I., Popov, P. Communications Biology, 9, 1039, 2026. 10.1038/s42003-026-10659-1
  • Computational methods for binding site prediction on macromolecules — Kozlovskii, I., Popov, P. Quarterly Reviews of Biophysics, 58, e12,
    1. 10.1017/S003358352500006X
  • Protein–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.1c00475
  • Spatiotemporal identification of druggable binding sites using deep learning — Kozlovskii, I., Popov, P. Communications Biology, 3, 618, 2020. 10.1038/s42003-020-01350-0

Nine publications total. The complete list, with the projects behind them, is on the Research page.

Technical areas

  • 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

Talks and posters

  • Identification of allosteric binding sites in KRAS using structure-based deep learning — 11th Moscow Conference on Computational Molecular Biology
  • Structure-based deep learning for binding site detection in nucleic acid macromolecules — ISMB/ECCB 2023 (2023)
  • Spatiotemporal identification of binding sites with computer vision — XXXVIII Symposium of Bioinformatics and Computer-Aided Drug Discovery, p. 53 (2022)

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.

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).

Teaching

  • Teaching Assistant, “Business Communication” course, Skoltech (2023)