Modeling the relative binding affinity of steroids to the progesterone receptor with probabilistic neural networks

Quantitative Structure-Activity Relationships

Volumn 20, Issue 3, 2001, Pages 223-226

  Niculescu, Stefan P ^a   Kaiser, Klaus L E ^b  

^a TerraBase Inc   (Canada)

^b ENVIRONMENT CANADA   (Canada)

Author keywords

Neural networks; Progesterone receptor; Relative binding affinity; Steroids; Structure activity

Indexed keywords

BENCHMARKING; BINDING ENERGY; LIPIDS; STATISTICS;

BASIC PROBABILISTIC NEURAL NETWORKS; DATA SET; GAUSSIAN KERNELS; MODELLING TOOLS; NEURAL-NETWORKS; PROBABILISTICS; PROGESTERONE RECEPTOR; RELATIVE BINDING AFFINITY; STEROID; STRUCTURE ACTIVITY;

NEURAL NETWORKS;

PROGESTERONE RECEPTOR; STEROID;

ARTICLE; ARTIFICIAL NEURAL NETWORK; HORMONE RECEPTOR INTERACTION; MOLECULAR MODEL; PRIORITY JOURNAL; PROBABILISTIC NEURAL NETWORK; RECEPTOR AFFINITY; STEROID BINDING; TECHNIQUE;

EID: 0035157708     PISSN: 09318771     EISSN: None     Source Type: Journal    
DOI: 10.1002/1521-3838(200110)20:3<223::AID-QSAR223>3.0.CO;2-D     Document Type: Article

References (10)

1. Prediction of the progesterone receptor binding of steroids using a combination of genetic algorithms and neural networks
2. Quantitative structure-activity relationship studies of progesterone receptor binding steroids
3. Estimation of a multivariate density
4. On estimation of probability density function and mode
5. Probabilistic neural networks
6. Modeling the toxicity of chemicals to Tetrahymena Pyriformis using molecular fragment descriptors and probabilistic neural networks
7. Modeling acute toxicity of chemicals to Daphnia Magna: A probabilistic neural network approach