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Sergei Nikolenko

👋 Hi there! I'm Sergei Nikolenko, a medicinal chemist deeply involved in computer-aided drug design. My passion lies in advancing molecular modeling and cheminformatics, using innovative technology to tackle intricate issues in the fields of medicine and pharmacology.

🌐 Explore my projects | Review my resume

🛠 Tech Stack

My technical expertise spans a wide array of tools and programming languages, enabling me to address diverse challenges in my field:

  • Programming Languages:
    • Python: A versatile tool for data analysis, molecular modeling, and machine learning tasks.
  • Molecular Modeling & Chemistry:
    • RDKit, Chemprop, ACE, DeepMD, BioPython
    • Molecular Dynamics: GROMACS, LAMMPS, Vina
    • Quantum Chemistry: MOPAC, ORCA, VASP
  • Machine Learning & Data Science:
    • Frameworks: PyTorch, TensorFlow
    • Libraries: scikit-learn, Cat/XGBoost, cuml, cudf
    • Data Handling: NumPy, Pandas
  • Utilities:
    • Job Scheduling: SLURM, screen
    • Scripting: Bash scripts for streamlining processes

📚 Highlight Projects

My career has been marked by numerous projects that not only posed significant challenges but also contributed to the field's advancement:

  • Molecular Reactivity Prediction:
    • Pioneered the use of graph convolutional networks for the prediction of chemical reactivity and Fukui indices.
  • Blood-Brain Barrier Penetration:
    • Applied cheminformatics techniques to predict the ability of small molecules to penetrate the BBB, a critical factor in CNS drug development.

Additionally, my scientific endeavors include:

  • Material Search & Structural Bioinformatics:
    • Conducted research on the structure and properties of molecular co-crystals and metal alloys, employing advanced molecular modeling systems.
    • Investigated receptor/ligand interactions through molecular docking and dynamics, contributing valuable insights to the field.

💬 Personal Development & Community Engagement

I am a proactive member of the scientific community, dedicated to continuous learning and knowledge sharing:

  • Languages: Fluent in English (Upper-Intermediate) and Russian (native), enabling effective international collaboration.
  • Kaggle: Achieved Expert status through active participation in data science competitions, honing my skills in real-world challenges. View my Kaggle profile

🌐 Connect With Me

Interested in my work or considering collaboration? Here's how you can reach me:

I'm always eager to explore new opportunities and ideas within medicinal chemistry and related domains. Feel free to get in touch!

Sergei Nikolenko's Projects

antibodycluster icon antibodycluster

The AntibodyCluster repository contains scripts designed to extract sequences of amino acid chains from antibodies present in Protein Data Bank (PDB) format files. The scripts employ the SAbDab database for file processing.

autogpt icon autogpt

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

chemprop icon chemprop

Message Passing Neural Networks for Molecule Property Prediction

deepchem icon deepchem

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

deepmd_crystal_generator icon deepmd_crystal_generator

This repository provides a toolkit for modeling co-crystals. It includes efficient scripts for DeepMD and ACE, optimized for cluster execution with slurm and screen.

descriptastorus icon descriptastorus

Descriptor computation(chemistry) and (optional) storage for machine learning

immunopeptidedesigner icon immunopeptidedesigner

Automated generation of immunogenic peptides from protein structures and molecular docking analysis using AlphaFold2 and AutodockVina.

quantumchemistrygang icon quantumchemistrygang

This repository is dedicated to the storage and documentation of computational assignments and projects related to the Quantum Chemistry course. It contains theoretical calculations, computational models, and analysis reports for various molecules.

single_cell_perturbations icon single_cell_perturbations

3rd place solution in Open Problems – Single-Cell Perturbations. This is a regression problem of 2 feature columns and 18211 targets.

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