- Home
- Publications
- Publication Search
- Publication Details
Title
An automated framework for QSAR model building
Authors
Keywords
Quantitative structure–activity relationship (QSAR), Machine learning, Feature selection, Variable importance, Random forests, Support vector machines, KNIME, Data set modelability
Journal
Journal of Cheminformatics
Volume 10, Issue 1, Pages -
Publisher
Springer Nature
Online
2018-01-16
DOI
10.1186/s13321-017-0256-5
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Chemical structure and correlation analysis of HIV-1 NNRT and NRT inhibitors and database-curated, published inhibition constants with chemical structure in diverse datasets
- (2017) Birgit Viira et al. JOURNAL OF MOLECULAR GRAPHICS & MODELLING
- Using Molecular Initiating Events to Develop a Structural Alert Based Screening Workflow for Nuclear Receptor Ligands Associated with Hepatic Steatosis
- (2016) Claire L. Mellor et al. CHEMICAL RESEARCH IN TOXICOLOGY
- Descriptors and their selection methods in QSAR analysis: paradigm for drug design
- (2016) Danishuddin et al. DRUG DISCOVERY TODAY
- Use of machine learning approaches for novel drug discovery
- (2016) Angélica Nakagawa Lima et al. Expert Opinion on Drug Discovery
- AutoQSAR: an automated machine learning tool for best-practice quantitative structure–activity relationship modeling
- (2016) Steven L Dixon et al. Future Medicinal Chemistry
- Kernel Target Alignment Parameter: A New Modelability Measure for Regression Tasks
- (2015) Gilles Marcou et al. Journal of Chemical Information and Modeling
- Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes
- (2015) Grace Patlewicz et al. REGULATORY TOXICOLOGY AND PHARMACOLOGY
- Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME
- (2015) George Nicola et al. Database-The Journal of Biological Databases and Curation
- eTOXlab, an open source modeling framework for implementing predictive models in production environments
- (2015) Pau Carrió et al. Journal of Cheminformatics
- A reliable computational workflow for the selection of optimal screening libraries
- (2015) Yocheved Gilad et al. Journal of Cheminformatics
- In Silico Machine Learning Methods in Drug Development
- (2014) Dimitar Dobchev et al. CURRENT TOPICS IN MEDICINAL CHEMISTRY
- Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology
- (2014) Kun-Yi Hsin et al. PLoS One
- Drug Discovery Applications for KNIME: An Open Source Data Mining Platform
- (2013) Michael P. Mazanetz et al. CURRENT TOPICS IN MEDICINAL CHEMISTRY
- Data Set Modelability by QSAR
- (2013) Alexander Golbraikh et al. Journal of Chemical Information and Modeling
- QSAR workbench: automating QSAR modeling to drive compound design
- (2013) Richard Cox et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Using Graph Indices for the Analysis and Comparison of Chemical Datasets
- (2013) Denis Fourches et al. Molecular Informatics
- The ChEMBL bioactivity database: an update
- (2013) A. Patrícia Bento et al. NUCLEIC ACIDS RESEARCH
- Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons
- (2013) Ana L Teixeira et al. Journal of Cheminformatics
- Machine Learning Techniques and Drug Design
- (2012) J.C. Gertrudes et al. CURRENT MEDICINAL CHEMISTRY
- Open PHACTS: semantic interoperability for drug discovery
- (2012) Antony J. Williams et al. DRUG DISCOVERY TODAY
- Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?
- (2012) Alexandre Varnek et al. Journal of Chemical Information and Modeling
- Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information
- (2011) Iurii Sushko et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- PubChem's BioAssay Database
- (2011) Y. Wang et al. NUCLEIC ACIDS RESEARCH
- The behaviour of random forest permutation-based variable importance measures under predictor correlation
- (2010) Kristin K Nicodemus et al. BMC BIOINFORMATICS
- In Silico Exploration for Identifying Structure-Activity Relationship of MEK Inhibition and Oral Bioavailability for Isothiazole Derivatives
- (2010) Georgia Melagraki et al. Chemical Biology & Drug Design
- Ligand - based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks
- (2010) Antreas Afantitis et al. EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
- Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach
- (2010) Shivani Agarwal et al. Journal of Chemical Information and Modeling
- Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
- (2010) Denis Fourches et al. Journal of Chemical Information and Modeling
- Best Practices for QSAR Model Development, Validation, and Exploitation
- (2010) Alexander Tropsha Molecular Informatics
- Variable selection using random forests
- (2010) Robin Genuer et al. PATTERN RECOGNITION LETTERS
- An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach
- (2010) Andrej-Nikolai Spiess et al. BMC Pharmacology & Toxicology
- Registration, Evaluation, and Authorization of Chemicals (REACH) Is but the First Step–How Far Will It Take Us? Six Further Steps to Improve the European Chemicals Legislation
- (2009) Christina Rudén et al. ENVIRONMENTAL HEALTH PERSPECTIVES
- Current Mathematical Methods Used in QSAR/QSPR Studies
- (2009) Peixun Liu et al. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
- Toxicology for the twenty-first century
- (2009) Thomas Hartung NATURE
- PubChem: a public information system for analyzing bioactivities of small molecules
- (2009) Y. Wang et al. NUCLEIC ACIDS RESEARCH
- How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
- (2009) J.C. Dearden et al. SAR AND QSAR IN ENVIRONMENTAL RESEARCH
- Conditional Variable Importance for Random Forests
- (2008) Carolin Strobl et al. BMC BIOINFORMATICS
- A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
- (2008) Alexander Statnikov et al. BMC BIOINFORMATICS
- Variable Selection Methods in QSAR: An Overview
- (2008) Maykel Gonzalez et al. CURRENT TOPICS IN MEDICINAL CHEMISTRY
- Are the Chemical Structures in Your QSAR Correct?
- (2008) Douglas Young et al. Quantitative structure-activity relationships & combinatorial science
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started