Name
Title | ||
|---|---|---|
Principal component analysis combined with truncated-Newton minimization for dimensionality reduction of chemical databases |
Abstract | ||
|---|---|---|
. The similarity and diversity sampling problems are two challenging optimization tasks that arise in the analysis of chemical
databases. As a first step to their solution, we propose an efficient projection/ refinement protocol based on the principal
component analysis (PCA) and the truncated-Newton minimization method implemented by our package TNPACK (PCA/TNPACK). We show
that PCA can provide the same initial guess as the singular value decomposition (SVD) for the optimization task of solving
the distance-geometry optimization problem if each column of a database matrix has a mean of zero. Hence, our PCA/TNPACK approach
is analogous to the SVD/TNPACK projection/refinement protocol that we developed recently for visualizing large chemical databases.
Using PCA/TNPACK and the Merck MDDR database (MDL Drug Data Report), we further investigate the projection/refinement procedure
with regards to the preservation of the original clusters of chemical compounds, the accuracy of similarity and diversity
sampling of chemical compounds, and the potential application in the study of structure activity relationships. We also explore
by simple experiments accuracy and efficiency aspects of the PCA/TNPACK procedure compared to those of a global optimization
algorithm (simulated annealing, as implemented by the program package SIMANN) in terms of producing the projection mapping
of a database. Numerical results show that the 2D PCA/TNPACK mapping can preserve the distance relationships of the original
database and is thus valuable as a first step in similarity and diversity applications. Of course, the generation of a global
rather than local minimizer and its interpretation in terms of pharameceutical applications remains a challenge. Since all
numerical tests are performed on the Merck MDDR database, results are representative of realistic cases encountered in the
field of drug design, and may help analyze properties of medicinal compounds. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/s10107-002-0345-7 | Math. Program. |
Keywords | Field | DocType |
Principal Component Analysis,Singular Value Decomposition,Diversity Sampling,Structure Activity Relationship,Global Optimization Algorithm | Simulated annealing,Singular value decomposition,Projection mapping,Mathematical optimization,Dimensionality reduction,Algorithm,Curse of dimensionality,Chemical database,Optimization problem,Principal component analysis,Mathematics | Journal |
Volume | Issue | ISSN |
95 | 1 | 0025-5610 |
Citations | PageRank | References |
3 | 0.44 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dexuan Xie | 1 | 67 | 10.92 |
| Suresh B. Singh | 2 | 29 | 4.62 |
| Eugene M. Fluder | 3 | 96 | 11.53 |
| Tamar Schlick | 4 | 251 | 62.71 |