Dataset Information

Large scale study of multiple-molecule queries.

ABSTRACT: BACKGROUND: In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family.Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics. RESULTS: Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics.Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (BKD), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data. CONCLUSION: Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from http://cdb.ics.uci.edu/.

SUBMITTER: Nasr RJ

PROVIDER: S-EPMC3225883 | biostudies-other | 2009

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Large scale study of multiple-molecule queries.

Nasr Ramzi J RJ Swamidass S Joshua SJ Baldi Pierre F PF

Journal of cheminformatics 20090604 1

<h4>Background</h4>In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family.Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the pre ...[more]

PMID: 20298525

Similar Datasets

Project description:BackgroundMultiple Sclerosis (MS) is an autoimmune neurodegenerative disease affecting approximately 3 million people globally. Despite rigorous research on MS, aspects of its development and progression remain unclear. Understanding molecular mechanisms underlying MS is crucial to providing insights into disease pathways, identifying potential biomarkers for early diagnosis, and revealing novel therapeutic targets for improved patient outcomes.MethodsWe utilized publicly available RNA-seq data (GSE138614) from post-mortem white matter tissues of five donors without any neurological disorder and ten MS patient donors. This data was interrogated for differential gene expression, alternative splicing and single nucleotide variants as well as for functional enrichments in the resulting datasets.ResultsA comparison of non-MS white matter (WM) to MS samples yielded differentially expressed genes involved in adaptive immune response, cell communication, and developmental processes. Genes with expression changes positively correlated with tissue inflammation were enriched in the immune system and receptor interaction pathways. Negatively correlated genes were enriched in neurogenesis, nervous system development, and metabolic pathways. Alternatively spliced transcripts between WM and MS lesions included genes that play roles in neurogenesis, myelination, and oligodendrocyte differentiation, such as brain enriched myelin associated protein (BCAS1), discs large MAGUK scaffold protein 1 (DLG1), KH domain containing RNA binding (QKI), and myelin basic protein (MBP). Our approach to comparing normal appearing WM (NAWM) and active lesion (AL) from one donor and NAWM and chronic active (CA) tissues from two donors, showed that different IgH and IgK gene subfamilies were differentially expressed. We also identified pathways involved in white matter injury repair and remyelination in these tissues. Differentially spliced genes between these lesions were involved in axon and dendrite structure stability. We also identified exon skipping events and spontaneous single nucleotide polymorphisms in membrane associated ring-CH-type finger 1 (MARCHF1), UDP glycosyltransferase 8 (UGT8), and other genes important in autoimmunity and neurodegeneration.ConclusionOverall, we identified unique genes, pathways, and novel splicing events affecting disease progression that can be further investigated as potential novel drug targets for MS treatment.

Project description:This study presents an analysis of the small molecule bioactivity profiles across large quantities of diverse protein families represented in PubChem BioAssay. We compared the bioactivity profiles of FDA approved drugs to non-FDA approved compounds, and report several distinct patterns characteristic of the approved drugs. We found that a large fraction of the previously reported higher target promiscuity among FDA approved compounds, compared to non-FDA approved bioactives, was frequently due to cross-reactivity within rather than across protein families. We identified 804 potentially novel protein target candidates for FDA approved drugs, as well as 901 potentially novel target candidates with active non-FDA approved compounds, but no FDA approved drugs with activity against these targets. We also identified 486348 potentially novel compounds active against the same targets as FDA approved drugs, as well as 153402 potentially novel compounds active against targets without active FDA approved drugs. By quantifying the agreement among replicated screens, we estimated that more than half of these novel outcomes are reproducible. Using biclustering, we identified many dense clusters of FDA approved drugs with enriched activity against a common set of protein targets. We also report the distribution of compound promiscuity using a Bayesian statistical model, and report the sensitivity and specificity of two common methods for identifying promiscuous compounds. Aggregator assays exhibited greater accuracy in identifying highly promiscuous compounds, while PAINS substructures were able to identify a much larger set of "middle range" promiscuous compounds. Additionally, we report a large number of promiscuous compounds not identified as aggregators or PAINS. In summary, the results of this study represent a rich reference for selecting novel drug and target protein candidates, as well as for eliminating candidate compounds with unselective activities.

Dataset Information

Large scale study of multiple-molecule queries.

Publications

Large scale study of multiple-molecule queries.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets