Generating Optimal Linear PLS Estimations (GOLPE): An Advanced Chemometric Tool for Handling 3D‐QSAR Problems

Quantitative Structure‐Activity Relationships

Volumn 12, Issue 1, 1993, Pages 9-20

  Baroni, Massimo ^a   Costantino, Gabriele ^b   Cruciani, Gabriele ^b   Riganelli, Daniela ^b   Valigi, Roberta ^b   Clementi, Sergio ^b  

^a Multivariate Infometric Analysis ^*  (Italy)

^b UNIVERSITY OF PERUGIA   (Italy)

Author keywords

3D QSAR; CoMFA; GOLPE; PLS; variable selection

Indexed keywords

ARTICLE; CONTROLLED STUDY; MATHEMATICAL MODEL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STATISTICAL ANALYSIS; TECHNIQUE;

EID: 0027310371     PISSN: 09318771     EISSN: 15213838     Source Type: Journal    
DOI: 10.1002/qsar.19930120103     Document Type: Article

References (36)

1. The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses
2. PLS regression methods
3. Validation of QSAR's
4. Crossvalidation, Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies
5. Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
6. Estimating Optimal Transformations for Multiple Regression and Correlation
7. Predictive ability of regression models. Part I: Standard deviation of prediction errors (SDEP)
8. ch. 6
9. Predictive ability of regression models. Part II: Selection of the best predictive PLS model
10. ch. 10
11. ch. 12