Clustering of small molecules: new perspectives and their impact on natural product lead discovery

Abstract

The clustering of small molecules is of considerable importance for computer-aided drug discovery and virtual screening applications. The structure of chemical data in appropriate subspaces of the chemical space is relevant to sample datasets in a representative manner, to generate small libraries with wide or narrow chemical coverage (depending on the specific goals), and to guide the selection of subsets of in silico hits that are submitted for experimental confirmation. In the field of natural products, identifying regions of the chemical space where bioactive compounds congregate and understanding the relationship between biosynthetic gene clusters and the molecular structure of secondary metabolites may have a direct impact on natural product discovery and engineering. Here, we briefly discuss general approximations and available resources for the clustering of small molecules, and how the clustering of small molecules can be boosted by the application of novel clustering approximations, namely subspace clustering and multi-view clustering, which represent opposite philosophies of the clustering paradigm. We present some specific applications of small molecule clustering in the field of natural products, and analyze how a chemogenomic perspective may be particularly embodied in the field of natural products.