Literature DB >> 2308139

Neural networks applied to structure-activity relationships.

T Aoyama1, Y Suzuki, H Ichikawa.   

Abstract

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Year:  1990        PMID： 2308139     DOI： 10.1021/jm00165a004

Source DB:  PubMed          Journal:  J Med Chem        ISSN： 0022-2623            Impact factor:   7.446


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  6 in total

1.  A machine learning approach to computer-aided molecular design.

Authors:  G Bolis; L Di Pace; F Fabrocini
Journal:  J Comput Aided Mol Des       Date:  1991-12       Impact factor: 3.686

2.  Data modelling with neural networks: advantages and limitations.

Authors:  D J Livingstone; D T Manallack; I V Tetko
Journal:  J Comput Aided Mol Des       Date:  1997-03       Impact factor: 3.686

3.  Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines.

Authors:  J D Hirst; R D King; M J Sternberg
Journal:  J Comput Aided Mol Des       Date:  1994-08       Impact factor: 3.686

4.  Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds.

Authors:  Elizabeth Goya Jorge; Anita Maria Rayar; Stephen J Barigye; María Elisa Jorge Rodríguez; Maité Sylla-Iyarreta Veitía
Journal:  Int J Mol Sci       Date:  2016-06-07       Impact factor: 5.923

Review 5.  Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.

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Journal:  Int J Mol Sci       Date:  2016-08-11       Impact factor: 5.923

Review 6.  Digital Pharmaceutical Sciences.

Authors:  Safa A Damiati
Journal:  AAPS PharmSciTech       Date:  2020-07-26       Impact factor: 3.246
  6 in total

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