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Expert Opin Drug Discov. 2010 Dec;5(12):1205-1220.

Exploiting PubChem for Virtual Screening.

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1
Department of Pharmaceutical Sciences, School of Pharmacy; Drug Discovery Institute/Pittsburgh Molecular Library Screening Center (PMLSC); Pittsburgh Chemical Methodologies & Library Development (PCMLD) Center; Departments of Computational Biology and Structural Biology; University of Pittsburgh, Pittsburgh, PA 15260, USA.

Abstract

IMPORTANCE OF THE FIELD:

PubChem is a public molecular information repository, a scientific showcase of the NIH Roadmap Initiative. The PubChem database holds over 27 million records of unique chemical structures of compounds (CID) derived from nearly 70 million substance depositions (SID), and contains more than 449,000 bioassay records with over thousands of in vitro biochemical and cell-based screening bioassays established, with targeting more than 7000 proteins and genes linking to over 1.8 million of substances.

AREAS COVERED IN THIS REVIEW:

This review builds on recent PubChem-related computational chemistry research reported by other authors while providing readers with an overview of the PubChem database, focusing on its increasing role in cheminformatics, virtual screening and toxicity prediction modeling.

WHAT THE READER WILL GAIN:

These publicly available datasets in PubChem provide great opportunities for scientists to perform cheminformatics and virtual screening research for computer-aided drug design. However, the high volume and complexity of the datasets, in particular the bioassay-associated false positives/negatives and highly imbalanced datasets in PubChem, also creates major challenges. Several approaches regarding the modeling of PubChem datasets and development of virtual screening models for bioactivity and toxicity predictions are also reviewed.

TAKE HOME MESSAGE:

Novel data-mining cheminformatics tools and virtual screening algorithms are being developed and used to retrieve, annotate and analyze the large-scale and highly complex PubChem biological screening data for drug design.

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