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AutoGrow specifications

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Unique identifier OMICS_16879
Name AutoGrow
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS
Programming languages Java, Python
License GNU General Public License version 3.0
Computer skills Advanced
Version 3.1
Stability Stable
Maintained Yes

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  • person_outline Jacob Durrant <>

Publications for AutoGrow

AutoGrow citations

 (8)
library_books

Computational methods in drug discovery

2016
PMCID: 5238551
PMID: 28144341
DOI: 10.3762/bjoc.12.267

[…] molecules that have features of the known ligands. the algorithm is able to identify novel ligands for several known drug targets that have predicted affinities higher than their known binders. the autogrow software is a drug molecule optimizing program. it can be used to optimize ligands according to various properties and binding affinities and is available to download []. if two fragments […]

library_books

Exploring the chemical space of influenza neuraminidase inhibitors

2016
PMCID: 4841240
PMID: 27114890
DOI: 10.7717/peerj.1958

[…] modality in the active site of neuraminidase. all protein structures were visualized and rendered in pymol, version 1.7.6.3., novel nais were generated via combinatorial library enumeration using autogrow 3.0 (; ) in which compounds are enumerated inside the binding pocket of influenza neuraminidase. autogrow is an evolutionary algorithm that generate populations of ligands through three […]

library_books

A Role for Fragment Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

2015
PMCID: 4566055
PMID: 26441817
DOI: 10.3389/fneur.2015.00197

[…] exponential number of diverse compounds. a set of 1500 compounds generated from 400 molecules was randomly assessed for quality by medicinal chemists, 94% of which were found to be acceptable ()., autogrow is another software package that incorporates medicinal chemistry knowledge into ligand design (). autogrow modifies the initial fragment through “mutations” that replace or combine reactive […]

library_books

Rational Design of Highly Potent and Slow Binding Cytochrome bc1 Inhibitor as Fungicide by Computational Substitution Optimization

2015
PMCID: 4549706
DOI: 10.1038/srep13471

[…] fungicide candidate also demonstrates a promising future of cso method. in fact, the commercial development of compound 18 in china is in progress., based on the modification and combination of autogrow and the amber 9.0 program, the cso protocol was designed to perform automatically computational substitution, energy minimization, and binding affinity evaluation. depicted […]

library_books

Sulfamethoxazole Induces a Switch Mechanism in T Cell Receptors Containing TCRVβ20 1, Altering pHLA Recognition

2013
PMCID: 3792127
PMID: 24116097
DOI: 10.1371/journal.pone.0076211

[…] computational method was developed to screen small peptide fragments. from this, a 15 amino acid peptide sequence, gatgrkcdgckhwha corresponding to human lamininα2, was determined using a modified autogrow [] script (http://sourceforge.net/p/autmatedpeptide) to generate random peptide fragments, and docking these using autodock vina [], to the tcr-hla structure. a complete screening method […]

library_books

Towards the development of novel Trypanosoma brucei RNA editing ligase 1 inhibitors

2011
PMCID: 3196686
PMID: 21878090
DOI: 10.1186/1471-2210-11-9

[…] brucei (t. brucei) is an infectious agent for which drug development has been largely neglected. we here use a recently developed computer program called autogrow to add interacting molecular fragments to s5, a known inhibitor of the validated t. brucei drug target rna editing ligase 1, in order to improve its predicted binding affinity., the proposed […]


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AutoGrow institution(s)
Department of Chemistry & Biochemistry, University of California San Diego, La Jolla, CA, USA; Department of Pharmacology, University of California San Diego, La Jolla, CA, USA; Department of Chemistry & Biochemistry, NSF Center for Theoretical Biological Physics, National Biomedical Computation Resource, University of California San Diego, La Jolla, CA, USA; Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA, USA
AutoGrow funding source(s)
Supported by NIH GM31749, NSF MCB-0506593, and MCA93S013, the Howard Hughes Medical Institute, the NSF Supercomputer Centers, the San Diego Supercomputer Center, the W.M. Keck Foundation, the National Biomedical Computational Resource, and the Center for Theoretical Biological Physics.

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