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Populating Chemical Space with Peptides using a Genetic Algorithm

revised on 29.11.2019, 08:54 and posted on 09.12.2019, 07:47 by Alice Capecchi, Alain Zhang, Jean-Louis Reymond
In drug discovery one uses chemical space as a concept to organize molecules according to their structures and properties. One often would like to generate new possible molecules at a specific location in chemical space marked by a molecule of interest. Herein we report the peptide design genetic algorithm (PDGA, code available at, a computational tool capable of producing peptide sequences of various chain topologies (linear, cyclic/polycyclic or dendritic) in proximity of any molecule of interest in a chemical space defined by MXFP, an atom-pair fingerprint describing molecular shape and pharmacophores. We show that PDGA generates high similarity analogs of bioactive peptides, including in selected cases known active analogs, as well as of non-peptide targets. We illustrate the chemical space accessible by PDGA with an interactive 3D-map of the MXFP property space available at PDGA should be generally useful to generate peptides at any location in chemical space.


Swiss National Science Foundation 200020_178998


Email Address of Submitting Author


University of Bern



ORCID For Submitting Author


Declaration of Conflict of Interest

The authors declare no Conflict of interest

Version Notes

In the second version on the manuscript paragraphs were extended and few points clarified.


Read the published paper

in Journal of Chemical Information and Modeling