Principal Component Analysis Combined with Truncated-Newton Minimization for Dimensionality Reduction of Chemical Databases

The similarity and diversity sampling problems are two challenging optimization tasks that arise in chemical database analyses. 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 program package TNPACK (PCA/TNPACK). We show here 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, PCA/TNPACK 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 compare the accuracy and efficiency of the PCA/TNPACK procedure to that of a global optimization algorithm (here we use the simulated annealing global optimization algorithm implemented by the program package SIMANN) in producing the projection mapping of 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. All numerical tests performed on the Merck MDDR database (MDL Drug Data Report) and thus represent realistic cases encountered in practice in the field of drug design.

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