Partitioning in binary-transformed chemical descriptor spaces.

Abstract

Here we describe a statistically based partitioning method called median partitioning (MP), which involves the transformation of value distributions of molecular property descriptors into a binary classification scheme. The MP approach fundamentally differs from other partitioning approaches that involve dimension reduction of chemical spaces such as cell-based partitioning, since MP directly operates in original, albeit simplified, chemical space. Modified versions of the MP algorithm have been implemented and successfully applied in diversity selection, compound classification, and virtual screening. These findings have demonstrated that dimension reduction techniques, although elegant in their design, are not necessarily required for effective partitioning of molecular datasets. An attractive feature of statistical partitioning approaches such as decision tree methods or MP is their computational efficiency, which is becoming an important criterion for the analysis of compound databases containing millions of molecules.