PubChem BioAssay: 2014 update

By Yanli Wang, Tugba Onal Suzek, Jian Zhang, Jiyao Wang, Siqian He, Tiejun Cheng, Benjamin A. Shoemaker, Asta Gindulyte, Stephen H. Bryant. 2013

264 citations recorded by the source corpus.

PubChem’s BioAssay database (http://pubchem.ncbi.nlm.nih.gov) is a public repository for archiving biological tests of small molecules generated through high-throughput screening experiments, medicinal chemistry studies, chemical biology research and drug discovery programs. In addition, the BioAssay database contains data from high-throughput RNA interference screening aimed at identifying critical genes responsible for a biological process or disease condition. The mission of PubChem is to serve the community by providing free and easy access to all deposited data. To this end, PubChem BioAssay is integrated into the National Center for Biotechnology Information retrieval system, making them searchable by Entrez queries and cross-linked to other biomedical information archived at National Center for Biotechnology Information. Moreover, PubChem BioAssay provides web-based and programmatic tools allowing users to search, access and analyze bioassay test results and metadata. In this work, we provide an update for the PubChem BioAssay resource, such as information content growth, new developments supporting data integration and search, and the recently deployed PubChem Upload to streamline chemical structure and bioassay submissions.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Zhihai Liu
2. Yan Li
3. Jianyang Zeng
4. Fangping Wan
5. A. Zhavoronkov
6. Jie Liu
7. Renxiao Wang
8. G. Wei

In Molecular Biology

1. Zhihai Liu
2. Yan Li
3. Jianyang Zeng
4. Fangping Wan
5. Renxiao Wang
6. G. Wei
7. Mohammad Mustafa Taye

Citations in the corpus

• PDB-wide collection of binding data: current status of the PDBbind database (2015, methodology): “To tackle this problem, some existing databases started to incorporate binding data; for example, ChEMBL (Gaulton et al ., 2012) and PubChem (Bolton et al ., 2008; Wang et al. , 2014) are perhaps the two most comprehensive databases in this category.”
• Deep learning with feature embedding for compound-protein interaction prediction (2016, methodology): “In our work, protein sequences that are used to learn the embeddings of protein features are retrieved from several databases, including PubChem [16], DrugBank [35], ChEMBL [36], Protein Data Bank [39] [40] [41] and UniProt [42].”
• The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology (2016, methodology): “In order to assess the biological relevance of the our results, Pubchem BioAssay database [26] was used to identify the compounds for which anticancer activities and other relevant biomedical properties of interest have been either tested or demonstrated.”