Literature DB >> 1507206

Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors.

S S So1, W G Richards.   

Abstract

A comparative study of quantitative structure-activity relationships involving diaminopyrimidines as DHFR inhibitors using regression analysis and the neural-network approach suggests that the neural network can outperform traditional methods. The technique permits the highlighting the functional form of those parameters which have an influence on the biological activity.

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Year:  1992        PMID： 1507206     DOI： 10.1021/jm00095a016

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


  16 in total

1.  A comparative study of ligand-receptor complex binding affinity prediction methods based on glycogen phosphorylase inhibitors.

Authors:  S S So; M Karplus
Journal:  J Comput Aided Mol Des       Date:  1999-05       Impact factor: 3.686

2.  Evaluation of designed ligands by a multiple screening method: application to glycogen phosphorylase inhibitors constructed with a variety of approaches.

Authors:  S S So; M Karplus
Journal:  J Comput Aided Mol Des       Date:  2001-07       Impact factor: 3.686

3.  CoMFA analysis of tgDHFR and rlDHFR based on antifolates with 6-5 fused ring system using the all-orientation search (AOS) routine and a modified cross-validated r(2)-guided region selection (q(2)-GRS) routine and its initial application.

Authors:  Aleem Gangjee; Xin Lin; Lisa R Biondo; Sherry F Queener
Journal:  Bioorg Med Chem       Date:  2010-01-06       Impact factor: 3.641

4.  QSAR modeling of AT1 receptor antagonists using ANN.

Authors:  Qing Su; Lu Zhou
Journal:  J Mol Model       Date:  2006-03-16       Impact factor: 1.810

5.  The discovery of indicator variables for QSAR using inductive logic programming.

Authors:  R D King; A Srinivasan
Journal:  J Comput Aided Mol Des       Date:  1997-11       Impact factor: 3.686

6.  Determination of receptor-bound drug conformations by QSAR using flexible fitting to derive a molecular similarity index.

Authors:  C A Montanari; M S Tute; A E Beezer; J C Mitchell
Journal:  J Comput Aided Mol Des       Date:  1996-02       Impact factor: 3.686

7.  Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions.

Authors:  Raquel Rodríguez-Pérez; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2020-05-02       Impact factor: 3.686

8.  Prediction of drug distribution in rat and humans using an artificial neural networks ensemble and a PBPK model.

Authors:  Paulo Paixão; Natália Aniceto; Luís F Gouveia; José A G Morais
Journal:  Pharm Res       Date:  2014-05-28       Impact factor: 4.200

9.  Quantitative structure-diastereoselectivity relationships for arylsulfoxide derivatives in radical chemistry.

Authors:  Mohamed Zahouily; Ahmed Rayadh; Mina Aadil; Driss Zakarya
Journal:  J Mol Model       Date:  2003-05-24       Impact factor: 1.810

10.  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
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