Literature DB >> 19065811

Associative neural network.

Igor V Tetko1.   

Abstract

An associative neural network (ASNN) is an ensemble-based method inspired by the function and structure of neural network correlations in brain. The method operates by simulating the short- and long-term memory of neural networks. The long-term memory is represented by ensemble of neural network weights, while the short-term memory is stored as a pool of internal neural network representations of the input pattern. The organization allows the ASNN to incorporate new data cases in short-term memory and provides high generalization ability without the need to retrain the neural network weights. The method can be used to estimate a bias and the applicability domain of models. Applications of the ASNN in QSAR and drug design are exemplified. The developed algorithm is available at http://www.vcclab.org.

Mesh:

Year:  2008        PMID: 19065811     DOI: 10.1007/978-1-60327-101-1_10

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  7 in total

1.  From bird's eye views to molecular communities: two-layered visualization of structure-activity relationships in large compound data sets.

Authors:  Shilva Kayastha; Ryo Kunimoto; Dragos Horvath; Alexandre Varnek; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2017-10-06       Impact factor: 3.686

2.  Theoretical and Experimental Studies of Phosphonium Ionic Liquids as Potential Antibacterials of MDR Acinetobacter baumannii.

Authors:  Larysa O Metelytsia; Diana M Hodyna; Ivan V Semenyuta; Vasyl V Kovalishyn; Sergiy P Rogalsky; Kateryna Yu Derevianko; Volodymyr S Brovarets; Igor V Tetko
Journal:  Antibiotics (Basel)       Date:  2022-04-06

3.  Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

Authors:  Iurii Sushko; Sergii Novotarskyi; Robert Körner; Anil Kumar Pandey; Matthias Rupp; Wolfram Teetz; Stefan Brandmaier; Ahmed Abdelaziz; Volodymyr V Prokopenko; Vsevolod Y Tanchuk; Roberto Todeschini; Alexandre Varnek; Gilles Marcou; Peter Ertl; Vladimir Potemkin; Maria Grishina; Johann Gasteiger; Christof Schwab; Igor I Baskin; Vladimir A Palyulin; Eugene V Radchenko; William J Welsh; Vladyslav Kholodovych; Dmitriy Chekmarev; Artem Cherkasov; Joao Aires-de-Sousa; Qing-You Zhang; Andreas Bender; Florian Nigsch; Luc Patiny; Antony Williams; Valery Tkachenko; Igor V Tetko
Journal:  J Comput Aided Mol Des       Date:  2011-06-10       Impact factor: 3.686

4.  Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process.

Authors:  Yurii Sushko; Sergii Novotarskyi; Robert Körner; Joachim Vogt; Ahmed Abdelaziz; Igor V Tetko
Journal:  J Cheminform       Date:  2014-12-11       Impact factor: 5.514

Review 5.  A Survey of Multi-task Learning Methods in Chemoinformatics.

Authors:  Sergey Sosnin; Mariia Vashurina; Michael Withnall; Pavel Karpov; Maxim Fedorov; Igor V Tetko
Journal:  Mol Inform       Date:  2018-11-28       Impact factor: 3.353

6.  How accurately can we predict the melting points of drug-like compounds?

Authors:  Igor V Tetko; Yurii Sushko; Sergii Novotarskyi; Luc Patiny; Ivan Kondratov; Alexander E Petrenko; Larisa Charochkina; Abdullah M Asiri
Journal:  J Chem Inf Model       Date:  2014-12-09       Impact factor: 4.956

7.  Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds.

Authors:  Elena S Salmina; Norbert Haider; Igor V Tetko
Journal:  Molecules       Date:  2015-12-23       Impact factor: 4.411

  7 in total

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