Abstract
High-throughput screening (HTS) techniques are increasingly being adopted by a variety of fields of toxicology. Notably, large-scale research efforts from government, industrial, and academic laboratories are screening millions of chemicals against a variety of biomolecular targets, producing an enormous amount of publicly available HTS assay data. These HTS assay data provide toxicologists important information on how chemicals interact with different biomolecular targets and provide illustrations of potential toxicity mechanisms. Open public data repositories, such as the National Institutes of Health’s PubChem (http://pubchem.ncbi.nlm.nih.gov), were established to accept, store, and share HTS data. Through the PubChem website, users can rapidly obtain the PubChem assay results for compounds by using different chemical identifiers (including SMILES, InChIKey, IUPAC names, etc.). However, obtaining these data in a user-friendly format suitable for modeling and other informatics analysis (e.g., gathering PubChem data for hundreds or thousands of chemicals in a modeling friendly format) directly through the PubChem web portal is not feasible. This chapter aims to introduce two approaches to obtain the HTS assay results for large datasets of compounds from the PubChem portal. First, programmatic access via PubChem’s PUG-REST web service using the Python programming language will be described. Second, most users, who lack programming skills, can directly obtain PubChem data for a large set of compounds by using the freely available Chemical In vitro–In vivo Profiling (CIIPro) portal (http://www.ciipro.rutgers.edu).
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Russo, D.P., Zhu, H. (2022). High-Throughput Screening Assay Profiling for Large Chemical Databases. In: Zhu, H., Xia, M. (eds) High-Throughput Screening Assays in Toxicology. Methods in Molecular Biology, vol 2474. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2213-1_12
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DOI: https://doi.org/10.1007/978-1-0716-2213-1_12
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