Using the BioAssay Ontology for analyzing high-throughput screening data.

High-throughput screening (HTS) is the main starting point for hit identification in drug discovery programs. This has led to a rapid increase of available screening data both within pharmaceutical companies and the public domain. We have used the BioAssay Ontology (BAO) 2.

Assessing HTS performance using BioAssay Ontology: screening and analysis of a bacterial phospho-N-acetylmuramoyl-pentapeptide translocase campaign.

With the public availability of biochemical assays and screening data constantly increasing, new applications for data mining and method analysis are evolving in parallel. One example is BioAssay Ontology (BAO) for systematic classification of assays based on screening setup and metadata annotations. In this article we report a high-throughput screening (HTS) against phospho-N-acetylmuramoyl-pentapeptide translocase (MraY), an attractive antibacterial drug target involved in peptidoglycan synthesis.

Transporter assays and assay ontologies: useful tools for drug discovery.

Transport proteins represent an eminent class of drug targets and ADMET (absorption, distribution, metabolism, excretion, toxicity) associated genes. There exists a large number of distinct activity assays for transport proteins, depending on not only the measurement needed (e.g.

On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors.

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp(3)-hybridized carbons (Fsp3), and fraction of molecular framework (f(MF)). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened.

Annotating Human P-Glycoprotein Bioassay Data.

Huge amounts of small compound bioactivity data have been entering the public domain as a consequence of open innovation initiatives. It is now the time to carefully analyse existing bioassay data and give it a systematic structure. Our study aims to annotate prominent in vitro assays used for the determination of bioactivities of human P-glycoprotein inhibitors and substrates as they are represented in the ChEMBL and TP-search open source databases.