You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

The discovery of phenylbenzamide derivatives as Grb7-based antitumor agents. Grb7 is a non-catalytic protein, the overexpression of which has been associated with the proliferative and migratory potentials of cancer cells. Virtual screening strategies involving a shape-based similarity search, molecular docking, and 2D-similarity searches complemented by experimental binding studies (Thermofluor and isothermal titration calorimetry) resulted in the identification of nine novel phenylbenzamide-based antagonists of the Grb7 SH2 domain. Moderate binding affinities were observed, ranging from K(d)=32.3 μM for lead phenylbenzamide NSC 104999 (1) to K(d)=1.1 μM for a structurally related compound, NSC 57148 (2). Deconvolution of the affinity data into its components revealed differences in lead binding, from being entropy based (lead 1) to enthalpically driven (NSC 100874 (3), NSC 55158 (4), and compound 2). Finally, the lead compound 1 was found to decrease the growth of MDA-MB-468 breast cancer cells, with an IC(50) value of 39.9 μM. It is expected that these structures will serve as novel leads in the development of Grb7-based anticancer therapeutics.
{"Chemical": ["NSC 104999", "NSC 55158", "NSC 57148", "phenylbenzamide", "NSC 100874"]}