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:

Learning from our mistakes: The 'unknown knowns' in fragment screening. In the past 15 years, fragment-based lead discovery (FBLD) has been adopted widely throughout academia and industry. The approach entails discovering very small molecular fragments and growing, merging, or linking them to produce drug leads. Because the affinities of the initial fragments are often low, detection methods are pushed to their limits, leading to a variety of artifacts, false positives, and false negatives that too often go unrecognized. This Digest discusses some of these problems and offers suggestions to avoid them. Although the primary focus is on FBLD, many of the lessons also apply to more established approaches such as high-throughput screening.
{"Chemical": []}