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AutoQSAR specifications


Unique identifier OMICS_18746
Name AutoQSAR
Software type Package/Module
Interface Command line interface
Restrictions to use License purchase required
Input data 1D, 2D or 3D structural data.
Operating system Unix/Linux, Mac OS, Windows
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


  • person_outline Matthew Repasky

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Publications for AutoQSAR

AutoQSAR citations


An automated framework for QSAR model building

J Cheminform
PMCID: 5770354
PMID: 29340790
DOI: 10.1186/s13321-017-0256-5

[…] e past decade, attempts have been made to attract the attentions towards the need of automation of the QSAR modelling process. More recently, Dixon et al. [] developed a machine-learning application (AutoQSAR) for automated QSAR modeling. It is unable to access data directly from online repositories and users required deep understanding to prepare a curated and standardized data set before modelin […]


QSAR workbench: automating QSAR modeling to drive compound design

J Comput Aided Mol Des
PMCID: 3657086
PMID: 23615761
DOI: 10.1007/s10822-013-9648-4

[…] ches to QSAR modeling are required to address these issues. The DiscoveryBus is one such system developed to allow for a more automated approach to model building through competitive workflow []. The AutoQSAR approach can automatically regenerate models as new data become available [, ]. AZOrange is an Open Source machine learning platform developed at AstraZeneca []. The Automated Modeling Enviro […]


QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality

J Comput Aided Mol Des
PMCID: 3639359
PMID: 23504478
DOI: 10.1007/s10822-013-9639-5

[…] The PD methods produce Gaussian-shaped predictive distributions, N(μpred, σpred). The mean values (μpred) are the predictions obtained from models, which were generated using AstraZeneca’s AutoQSAR system []. We used 4 different machine learning algorithms that are available in R (v2.14.0): [] Partial Least Squared (PLS); k-Nearest Neighbours (KNN); Random Forests (RF); and Support Vect […]

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AutoQSAR institution(s)
Schrödinger, Inc., New York, NY, USA; Schrödinger GmbH, Mannheim, Baden-Württemberg, Germany; Schrödinger, Inc., Portland, OR, USA
AutoQSAR funding source(s)
Supported by Schrödinger, Inc.

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