Literature DB >> 11410065

Quantitative structure-activity relationship studies using Gaussian processes.

F R Burden1.   

Abstract

A Gaussian process method (GPM) is described and applied to the production of some QSAR models. These models have the potential to solve a number of problems which arise in QSAR modeling in that no parameters have to be supplied and only one hyperparameter is used in finding the optimal solution. The application of the method to QSAR is illustrated using data sets of compounds active at the benzodiazepine and muscarinic receptors as well as the data set of the toxicity of substituted benzenes to the ciliate, Tetrahymena Pyriformis.

Entities:  

Year:  2001        PMID: 11410065     DOI: 10.1021/ci000459c

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  6 in total

1.  Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility.

Authors:  Olga Obrezanova; Joelle M R Gola; Edmund J Champness; Matthew D Segall
Journal:  J Comput Aided Mol Des       Date:  2008-02-14       Impact factor: 3.686

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

Authors:  David J Wood; Lars Carlsson; Martin Eklund; Ulf Norinder; Jonna Stålring
Journal:  J Comput Aided Mol Des       Date:  2013-03-16       Impact factor: 3.686

3.  Comparative performance of descriptors in a multiple linear and Kriging models: a case study on the acute toxicity of organic chemicals to algae.

Authors:  Gulcin Tugcu; H Birkan Yilmaz; Melek Türker Saçan
Journal:  Environ Sci Pollut Res Int       Date:  2014-06-21       Impact factor: 4.223

4.  A classification study of respiratory Syncytial Virus (RSV) inhibitors by variable selection with random forest.

Authors:  Ming Hao; Yan Li; Yonghua Wang; Shuwei Zhang
Journal:  Int J Mol Sci       Date:  2011-02-21       Impact factor: 5.923

5.  Proteochemometric modeling in a Bayesian framework.

Authors:  Isidro Cortes-Ciriano; Gerard Jp van Westen; Eelke Bart Lenselink; Daniel S Murrell; Andreas Bender; Thérèse Malliavin
Journal:  J Cheminform       Date:  2014-06-28       Impact factor: 5.514

Review 6.  Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.

Authors:  Hyunho Kim; Eunyoung Kim; Ingoo Lee; Bongsung Bae; Minsu Park; Hojung Nam
Journal:  Biotechnol Bioprocess Eng       Date:  2021-01-07       Impact factor: 3.386

  6 in total

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