Icolos: A workflow manager for structure based post-processing of de novo generated small molecules

01 February 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


We present Icolos, a workflow manager written in Python as a tool for automating complex structure-based workflows. Icolos can be used as a standalone tool, for example in virtual screening campaigns, or can be used in conjunction with deep learning-based molecular generation facilitated for example by REINVENT, a previously published de novo design package. In this publication, we focus on the internal structure and general capabilities of Icolos, using docking experiments as an illustration. The source code is freely available at https://github.com/MolecularAI/Icolos under the Apache 2.0 licence. Tutorial notebooks containing minimal working examples can be found at https://github.com/MolecularAI/IcolosCommunity.


Molecular Dynamics
Computational Biophysics
Machine Learning
Molecular Docking
Workflow manager
De novo design
Structure based drug design
Generative Models

Supplementary materials

Manual for docking workflow steps
Contains explanations and instructions for workflow steps (docking) referred to in the main manuscript

Supplementary weblinks


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