Extracting SAR Information from a Large Collection of Anti-Malarial Screening Hits by NSG-SPT Analysis

Abstract

We combine two graphical SAR analysis methods, Network-like Similarity Graphs (NSGs) and Similarity-Potency Trees (SPTs), to search for SAR information in a large and heterogeneous compound data set containing more than 13,000 antimalarial screening hits that was recently released by GlaxoSmithKline (GSK). The NSG-SPT approach first identifies subsets of compounds inducing local SAR discontinuity in data sets and then extracts available SAR information from these subsets in a graphically intuitive manner. Applying the NSG-SPT analysis scheme, we have identified in the GSK collection compound subsets of high local SAR information content including both known and previously unknown antimalarial chemotypes, which yielded interpretable SAR patterns. This information should be helpful to prioritize and select antimalarial candidate compounds for further chemical exploration. Furthermore, the NSG-SPT tools are publicly available, and our study also shows how to practically apply these SAR analysis methods to study large compound data sets.

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

“…These nodes can be used for ‘hopping’ between sub-networks having different chemical characteristics. Searching for nodes with high centrality and a closer look at their properties may contribute to the discovery of new drug candidates and uncover new directions for mechanistic-, scaffold- or target-hopping approaches (Gonzalez-Diaz & Prado-Prado, 2008; Hert et al, 2008; Prado-Prado et al, 2008; Wawer et al, 2008; Bajorath et al, 2009; Prado-Prado et al, 2009; Gonzalez-Diaz et al, 2010a; Peltason et al, 2010; Prado-Prado et al, 2010; Wawer et al, 2010; Iyer et al, 2011a; Iyer et al, 2011b; Iyer et al, 2011c; Krein & Sukumar, 2011; Wawer & Bajorath, 2011a; Wawer & Bajorath, 2011b). SARANEA (http://www.limes.uni-bonn.de/forschung/abteilungen/Bajorath/labwebsite/downloads/saranea/view) is a freely available program to mine structure-activity and structure-selectivity relationship information in compound data sets (Lounkine et al, 2010).…”

An Invitation to Open Innovation in Malaria Drug Discovery: 47 Quality Starting Points from the TCAMS

“…This letter describes our strategy in mining TCAMS to identify potential starting points for lead optimization programs. An elegant structureÀactivity relationship (SAR) analysis of TCAMS has already been described by Wawer and Bajorath; 13 however, our priority in this work is different in that we use ABSTRACT: In 2010, GlaxoSmithKline published the structures of 13533 chemical starting points for antimalarial lead identification. By using an agglomerative structural clustering technique followed by computational filters such as antimalarial activity, physicochemical properties, and dissimilarity to known antimalarial structures, we have identified 47 starting points for lead optimization.…”

Chemoinformatics Profiling of Ionic Liquids Cytotoxicity—From Machine Learning to Network-Like Similarity Graphs

“…The most significant cytotoxicity cliff pair in this network (see Figure 2) it is constituted by N-Methyl-N,N-dioctyl-1-octanaminium bis(trifluoromethylsulfonyl)imide and N-Ethyl-N,N-dimethyl-1-butanaminium bis(trifluoromethylsulfonyl)imide (nodes 1 and 2 in the network) which is a clear example of the influence of the alkyl side chain length over the cytotoxicity of ILs [6][7][8][9][10].…”