Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens
Fig 1
Overview of the CSNAP approach for high-throughput compound target identification using Chemical Similarity Networks (CSNs).
(A) Discovery of diverse ligands from cell-based screens with unknown cellular targets. Note that structurally distinct compound classes are represented by different shapes, while structurally-related analogs within each class are labeled with different colors. (B) Target identification using CSNAP. Bioactivity database searches to identify structurally similar reference compounds with known target annotations. The grey nodes represent target annotated compounds. (C) A pair-wise similarity matrix was computed by considering both intra and inter-ligand similarity between query and reference compounds using Tanimoto coefficient (Tc) with cutoff > 0.7. (D) Structurally diverse ligands are clustered into chemical similarity subnetworks based on representative chemotypes (consensus chemical patterns). (E) The network topology was used to guide and quantify the protein-ligand interactions for drug target prediction. Two neighbor counting functions, S-score and H-score were applied to identify and rank the most common targets among the first-order neighbors of the query compounds within the CSN. In this example, compound α has a consensus Target A score = 3 and a Target C score = 1, whereas compound β has a consensus score = 1 for Target A, B and C. (F) Experimental target validation. The predicted targets were validated by comparing RNAi with compound-induced cellular phenotypes and by testing direct protein-ligand interactions in in-vitro assays.