This document discusses the extraction of key relationships (D-A-R-C-P) reported in biomedical literature where a bioactivity (A) and result (R) are reported for a chemical structure (C) that modulates a protein target (P). It analyzes the statistics of DARCP entity accumulation from three manually curated databases and compares it to PubChem. While public databases have captured around 18% of known human protein targets, commercial databases have captured around 4 times more DARCP relationships through greater curation resources. The future of DARCP extraction depends on increased natural language processing, open access policies, and databases facilitating the input of these relationships.