Multidimensional scaling of combinatorial libraries without explicit enumeration

A novel approach for the multidimensional scaling of large combinatorial libraries is presented. The method employs a multilayer perceptron, which is trained to predict the coordinates of the products on the nonlinear map from pertinent features of their respective building blocks. This method limits the expensive enumeration and descriptor generation to only a small fraction of products and, in addition, relieves the enormous computational effort required for the low-dimensional embedding by conventional iterative multidimensional scaling algorithms. In effect, the method provides an explicit mapping function from reagents to products, and allows the vast majority of compounds to be projected without constructing their connection tables. The advantages of this approach are demonstrated using two combinatorial libraries based on the reductive amination and Ugi reactions, and three descriptor sets that are commonly used in similarity searching, diversity profiling and structure–activity correlation. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1712–1722, 2001