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.

ProLIF

Description

ProLIF is a tool designed to generate Interaction FingerPrints (IFP) and compute similarity scores for protein-ligand interactions, given a reference ligand and a list of binding-site residues.

Skills

  • Writing an object oriented program
  • Designing a command line interface
  • Developping unit and integration tests, automatize their execution with Travis-CI, and assess the coverage of the test suite with Coveralls.io
  • Uploading a project to the Python Package Index (PyPI)
  • Approximating interactions between a protein and a ligand from structural data
  • Encoding chemistry/biology information as a bit string

Tools

Python: rdkit, unittest, setuptools, argparse
Web: Travis-CI, Coveralls.io

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.