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AutoGrow 4.0 is a complete rewrite of the evolutionary algorithm that optimizes candidate ligands for predicted binding affinity and other druglike properties. Though no substitute
for the medicinal chemist, AutoGrow attempts to introduce some chemical
intuition into the automated optimization process. By carefully crafting
chemically feasible druglike molecules, we hope that AutoGrow 4.0 will help
supplement the chemist's efforts.
# AutoGrow 4.0
AutoGrow 4.0 is a complete rewrite of the evolutionary algorithm that optimizes candidate ligands for predicted binding affinity and other druglike properties. Though no substitute for the medicinal chemist, AutoGrow attempts to introduce some chemical intuition into the automated optimization process. By carefully crafting chemically feasible druglike molecules, we hope that AutoGrow 4.0 will help supplement the chemist's efforts.
The entire code base of Autogrow were rewritten from the previous release of 3.1.2. Autogrow 4.0 takes advantage of the recent advancements in multiprocessing, cheminformatics, and docking. Autogrow 4.0 handles all ligand manipulations using 1D SMILES strings using the API RDKit, which is faster and more maintainable than handling manipulations in 3D using PDB's. As docking softwares require 3D versions of the file, we use Gypsum to convert the 1D SMILES to 3D and Protanination to handle the protanation of the ligands using biologically relevant pH settings.
Autogrow 4.0 takes advantage of the recent advancements in multiprocessing, cheminformatics, and docking. Autogrow 4.0 handles all ligand manipulations using 1D SMILES strings using the API RDKit, which is faster and more maintainable than handling manipulations in 3D using PDB's. As docking softwares require 3D versions of the file, we use Gypsum to convert the 1D SMILES to 3D and Protanination to handle the protanation of the ligands using biologically relevant pH settings.
Autogrow 4.0 was designed with modularity in mind. We've expanded new user options for drug-likeliness, reaction library options for Crossover, docking executable softwares, ranking, and selection algorithms. All of these as well as scoring functions have been designed to allow the user to add their own custom options. Please see the tutorial for how and where to expand these options. We hope Autogrow will become a living code base.
A detailed tutorial for using AutoGrow can be found in the directory autogrow4/tutorial/tutorial.md
Authors:
Jacob Spiegel
Patrick Ropp
Jacob Durrant
Jacob O. Spiegel
Jacob D. Durrant
Acknowledgments:
Erich Helleman
Kevin Cassidy
Harrison Green
Harrison Green
Erich Helleman
Patrick J. Ropp
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