ProLIF

Description

ProLIF is a tool designed to generate interaction fingerprints for protein-ligand and protein-protein interactions extracted from molecular dynamics trajectories and docking simulations. It also supports DNA-ligand, DNA-protein and DNA-DNA interactions.

Skills

  • Developping an object oriented program
  • Writing unit and integration tests with pytest
  • Deploying a continuous integration pipeline with GitHub Actions
  • Documenting code with sphinx and publishing it on ReadTheDocs
  • Adding interactive notebooks through Binder
  • Uploading a module to the Python Package Index (PyPI)
  • Approximating interactions between molecules from structural data

Tools

Python: rdkit, mdanalysis, pandas, numpy, pytest, sphinx, setuptools
CI: GitHub Actions, ReadTheDocs, Binder

ChemFlow

Description

ChemFlow is a series of computational chemistry workflows:

  • designed to automatize and simplify the drug discovery pipeline and scoring function benchmarking
  • including extensive data analysis and reporting tools for rapid decision making
  • adapted to work locally or on compute clusters

Skills

  • Collaborating with other developpers and users
  • Converting a protocol to a workflow
  • Designing user-friendly interfaces: command-line and graphical
  • Self-teaching
  • Implementing parallel calculations on different systems (PC, cluster)
  • Visualizing chemistry data efficiently
  • Documenting a software
  • Writing tutorials

Tools

  • Programming:
    • Shell: bash, awk, sed, git
    • Python: Jupyter-notebooks, pandas, numpy, sklearn, scipy, rdkit, matplotlib, seaborn, PyQt5, PyInstaller, concurrent
  • Docking: PLANTS, Vina
  • Molecular Dynamics: Amber16
  • Visualization: PyMOL, VMD
  • Versioning: Git
  • Job scheduler: PBS, SLURM

Contributors

Diego Enry Barreto Gomes, Paulina Pacak, Nicolas Martin, and Adrien Cerdan. Supervised by Marco Cecchini.

Interference of H2 with the e- transfer to colloidal Pt catalyst

Description

This work is based on the following publication: J. Phys. Chem. 88, 18, 4131-4135.
Pr. Thomas Ebbesen proposed volunteer Chemoinformatics students to model the evolution of a complex chemical system during our "Kinetics of complex systems" lectures.

Skills

  • Extracting data from a publication
  • Solving ordinary differential equations (ODE)
  • Modeling a complex kinetic system

Tools

Python: numpy, scipy, matplotlib

Contributors

Philipe Gantzer, Balthazar Omeyer, and Thomas Mangin. Supervised by Thomas Ebbesen.