Literature DB >> 7815093

Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines.

J D Hirst1, R D King, M J Sternberg.   

Abstract

One of the largest available data sets for developing a quantitative structure-activity relationship (QSAR)--the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives--has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.

Entities:  

Mesh:

Substances:

Year:  1994        PMID: 7815093     DOI: 10.1007/bf00125376

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  6 in total

1.  Correlation analysis. Its application to the structure-activity relationship of triazines inhibiting dihydrofolate reductase.

Authors:  C Silipo; C Hansch
Journal:  J Am Chem Soc       Date:  1975-11-12       Impact factor: 15.419

2.  Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors.

Authors:  T A Andrea; H Kalayeh
Journal:  J Med Chem       Date:  1991-09       Impact factor: 7.446

3.  Quantitative structure-activity relationship of reversible dihydrofolate reductase inhibitors. Diaminotriazines.

Authors:  C Hansch; C Silipo
Journal:  J Med Chem       Date:  1974-07       Impact factor: 7.446

4.  Correlation analysis of Baker's studies on enzyme inhibition. 1. Guanine deaminase, xanthine oxidase, dihydrofolate reductase, and complement.

Authors:  C Silipo; C Hansch
Journal:  J Med Chem       Date:  1976-01       Impact factor: 7.446

5.  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

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

Authors:  S S So; W G Richards
Journal:  J Med Chem       Date:  1992-08-21       Impact factor: 7.446

  6 in total
  7 in total

1.  Warmr: a data mining tool for chemical data.

Authors:  R D King; A Srinivasan; L Dehaspe
Journal:  J Comput Aided Mol Des       Date:  2001-02       Impact factor: 3.686

2.  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

3.  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

4.  Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

Authors:  R D King; S H Muggleton; A Srinivasan; M J Sternberg
Journal:  Proc Natl Acad Sci U S A       Date:  1996-01-09       Impact factor: 11.205

5.  Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming.

Authors:  R D King; A Srinivasan
Journal:  Environ Health Perspect       Date:  1996-10       Impact factor: 9.031

6.  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

7.  Drug design for ever, from hype to hope.

Authors:  G Seddon; V Lounnas; R McGuire; T van den Bergh; R P Bywater; L Oliveira; G Vriend
Journal:  J Comput Aided Mol Des       Date:  2012-01-18       Impact factor: 3.686

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.