Data Mining of Protein-Binding Profiling Data Identifies Structural Modifications that Distinguish Selective and Promiscuous Compounds

Activity profiling of compound collections across multiple targets is increasingly being used in probe and drug discovery. Herein, we discuss an approach to systematically analyzing the structure–activity relationships of a large screening profile data with emphasis on identifying structural changes that have a significant impact on the number of proteins to which a compound binds. As a case study, we analyzed a recently released public data set of more than 15 000 compounds screened across 100 sequence-unrelated proteins. The screened compounds have different origins and include natural products, synthetic molecules from academic groups, and commercial compounds. Similar synthetic structures from academic groups showed, overall, greater promiscuity differences than do natural products and commercial compounds. The method implemented in this work readily identified structural changes that differentiated highly specific from promiscuous compounds. This approach is general and can be applied to analyze any other large-scale protein-binding profile data

Characterization of a comprehensive flavor database

Flavor perception involves, among a number of physiological and psychological processes, the recognition of chemicals by olfactory and taste receptors. The highly complex and multidimensional nature of flavor perception challenges our ability to both predict and design new flavor entities. Toward this endeavor, classifications of flavor descriptors have been proposed. Here, we developed a fingerprint‐based representation of a large data set comprising 4181 molecules taken from the commercially available Leffingwell & Associates Canton, Georgia, USA database marketed as Flavor‐Base Pro©2010. Flavor descriptions of the materials in this database were composite descriptions, collected from numerous sources over the course of more than 40 years. The flavor descriptors were referenced against a detailed and authoritative sensory lexicon (ASTM, American Society for Testing and Materials publication DS 66) comprising 662 flavor attributes. Comparison of clustering analysis, principal component analysis, and descriptor associations provided similar conclusions for various mutually correlated descriptors. Regarding analysis of the flavor similarity of the molecules, the clustering performed provided a means for the quick selection of molecules with either high or low flavor similarity description. Preliminary comparison of the chemical structures to the flavor description demonstrated the feasibility but also the complexity of this task. Additional studies including different structural representations, careful selection of subsets from this data set, as well as the use of a number of classification methods will demonstrate the utility of structure–flavor associations. This work shows that the flavor information contained in databases, such as that used in the present study, can be analyzed following standard chemoinformatics methods