1. A method for determining properties of products from a combinatorial chemical library P using features of their respective building blocks, the method comprising the steps of: (1) determining at least one feature for each building block in the combinatorial library P, &lcub;a ijk, i&equals;1,2,...,r; j&equals;1,2,...,r l ; k&equals;1, 2,..., n i &rcub;, wherein r represents the number of variation sites in the combinatorial library, r i represents the number of building blocks at the i-th variation site, and ni represents the number of features used to characterize each building block at the i-th variation site; (2) selecting a training subset of products &lcub;p l, i&equals;1,2,...,m; p i &egr;P&rcub; from the combinatorial library P; (3) determining q properties for each compound pi in the selected training subset of products, wherein y i &equals;&lcub;y ij, i&equals;1,2,...,m, j&equals;1,2,...,q&rcub; represents the determined properties of compound p i, and wherein q is greater or equal to one; (4) identifying, for each product p l of the training subset of products, the corresponding building blocks &lcub;t ij, t ij &equals;1, 2,..., r j, j&equals;1, 2,..., r&rcub; and concatenating their features determined in step (1) into a single vector &lcub;x i &equals;a 1t i &verbar;a 2t i2 &verbar;... &verbar;a rt ir &rcub;; (5) using a supervised machine learning approach to infer a mapping function f that transforms input values x i to output values y i from the input/output pairs in the training set T&equals;&lcub;(x i, y i ), i&equals;1,2,...,m&rcub;; (6) identifying, after the mapping function f is determined, for a product p z &egr;P, the corresponding building blocks &lcub;t zj, j&equals;1, 2,..., r&rcub; and concatenating their features, a 1t z1, a 2t z2,..., a rt zr, into a single vector &lcub;x z &equals;a 1t 1 &verbar;a 2t 2 &verbar;... &verbar;a rt r&rcub;, and (7) mapping x z →y z, using the mapping function f determined in step (5), wherein y z represents the properties of product p z.