✍ Scribed by Giovanni Cincilla; Michael Thormann; Miquel Pons
No coin nor oath required. For personal study only.
The ensemble of conceivable molecules is referred to as the Chemical Space. In this article we describe a hierarchical version of the Affinity Propagation (AP) clustering algorithm and apply it to analyze the LINGO‐based similarity matrix of a 500 000‐molecule subset of the PubChem database, which contains more than 19 million compounds. The combination of two highly efficient methods, namely the AP clustering algorithm and LINGO‐based molecular similarity calculations, allows the unbiased analysis of large databases. Hierarchical clustering generates a numerical diagonalization of the similarity matrix. The target‐independent, intrinsic structure of the database , derived without any previous information on the physical or biological properties of the compounds, maps together molecules experimentally shown to bind the same biological target or to have similar physical properties.
📜 SIMILAR VOLUMES
## Abstract The emerging picture of a continuous protein fold space highlights the existence of non obvious structural similarities between proteins with apparent different topologies. The identification of structure resemblances across fold space and the analysis of similar recognition regions may