AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization
Abstract We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely no...
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doaj-7cce4e0db5d64d7e83f78e6dc9d1c40e2020-11-25T03:00:27ZengBMCJournal of Cheminformatics1758-29462020-04-0112111610.1186/s13321-020-00429-4AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimizationJacob O. Spiegel0Jacob D. Durrant1Department of Biological Sciences, University of PittsburghDepartment of Biological Sciences, University of PittsburghAbstract We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4 .http://link.springer.com/article/10.1186/s13321-020-00429-4AutogrowGenetic algorithmComputer-aided drug designVirtual screeningPARP-1 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jacob O. Spiegel Jacob D. Durrant |
spellingShingle |
Jacob O. Spiegel Jacob D. Durrant AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization Journal of Cheminformatics Autogrow Genetic algorithm Computer-aided drug design Virtual screening PARP-1 |
author_facet |
Jacob O. Spiegel Jacob D. Durrant |
author_sort |
Jacob O. Spiegel |
title |
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization |
title_short |
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization |
title_full |
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization |
title_fullStr |
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization |
title_full_unstemmed |
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization |
title_sort |
autogrow4: an open-source genetic algorithm for de novo drug design and lead optimization |
publisher |
BMC |
series |
Journal of Cheminformatics |
issn |
1758-2946 |
publishDate |
2020-04-01 |
description |
Abstract We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4 . |
topic |
Autogrow Genetic algorithm Computer-aided drug design Virtual screening PARP-1 |
url |
http://link.springer.com/article/10.1186/s13321-020-00429-4 |
work_keys_str_mv |
AT jacobospiegel autogrow4anopensourcegeneticalgorithmfordenovodrugdesignandleadoptimization AT jacobddurrant autogrow4anopensourcegeneticalgorithmfordenovodrugdesignandleadoptimization |
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1724698102166716416 |