<i>IcePick</i>:  A Flexible Surface-Based System for Molecular Diversity

Mount, John; Ruppert, Jim; Welch, Will; Jain, Ajay N.

doi:10.1021/jm970775r

Cited by 57 publications

(49 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditionally, the approach of minimum spanning trees has been applied to a limited extent to cluster chemical libraries and to select quantitative structure-activity relationship (QSAR) subseries from a larger data set. [11][12][13][14][15][16][17] Recently, studies have been conducted to relate protein similarity to the structural similarity of the ligands of those proteins. 18 Although the resulting networks contained only proteins, the ligand molecules were represented by their average structural similarity; thus, they were incorporated indirectly into the graphs.…”

Section: Introductionmentioning

confidence: 99%

Finding Key Members in Compound Libraries by Analyzing Networks of Molecules Assembled by Structural Similarity

Lepp

Huang

Okada

2009

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Characterization of chemical libraries is an essential task in everyday chemoinformatics practice. This study describes some potential uses of network visualization and analysis methods to identify distinguished members of compound libraries. Molecules were ordered into networks by their structural similarity defined by molecular fingerprints. Various properties of such networks were examined. It was shown, that the correlation methods used to calculate the similarity between two structures radically determined the topology of networks. From the same set of molecules, the Russel-Rao and the Baroni-Urbani methods created sparser and denser networks, respectively, than using the Tanimoto method. Central nodes, corresponding to central compounds in the libraries, were determined for some example data sets. It was shown by the case of adenosine A1, A2, and dual antagonists that the methods used to identify central nodes could be divided into two groups: (1) centrality methods, exemplified by the centroid centrality, which could pick up structures that were the most similar to the largest number of other molecules and (2) group, exemplified by betweenness centrality, that could identify molecules that had intermediate structures between some homogeneous subsets of the library. The latter method gave significantly higher ranks to dual adenosine antagonists, hinting the suitability of this measure to identify molecules with multiple activities. Some practical applications of the method for clustering of and sample selection from chemical libraries are presented. In the frame of the study, a Jchem plug-in has been developed to the Cytoscape network visualization software, which makes the visual observation of molecular networks more convenient. The plug-in is included in the Supporting Information of the article for free usage.

show abstract

Section: Introductionmentioning

confidence: 99%

Finding Key Members in Compound Libraries by Analyzing Networks of Molecules Assembled by Structural Similarity

Lepp

Huang

Okada

2009

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…A full analysis of whether the use of "high-resolution" QSCD is practical as a diversity tool compared to other highresolution methods (e.g. ref 43) is a subject of current research.…”

Section: Resultsmentioning

confidence: 99%

Quantized Surface Complementarity Diversity (QSCD): A Model Based on Small Molecule−Target Complementarity

Wintner¹,

Moallemi²

2000

J. Med. Chem.

View full text Add to dashboard Cite

A model of molecular diversity is presented. The model, termed "Quantized Surface Complementarity Diversity" (QSCD), defines molecular diversity by measuring molecular complementarity to a fully enumerated set of theoretical target surfaces. Molecular diversity space is defined as the molecular complement to this set of enumerated surfaces. Using a set of known test compounds, the model is shown to be biologically relevant, consistently scoring known actives as similar. At the resolution of the model, which examines molecules "quantized" into 4.24 Å cubic units and treats four points of specific energetic complementarity, the minimum number of compounds needed to fully cover molecular diversity space up to volume 1070 cubic Å is estimated to be on the order of 24 million molecules. Most importantly, QSCD allows for individual points in diversity space to be filled by direct modeling of molecular libraries into detailed 3D templates of shape and functionality.

show abstract

“…8 A molecular imprint is a vector of similarity values for a particular molecule against a fixed basis set of molecules (one pose each). Such vectors have precedent in predicting many molecular properties, 14,15 and the conformational pre-search procedure was augmented to produce standard molecular imprints. So, given two pre-searched molecules, their mutual maximal 3D similarity can be rapidly calculated, and the p -value conversion is immediately derived from the estimated distributional parameters for each molecule.…”

Section: Methods and Datamentioning

confidence: 99%

Prediction of Off-Target Drug Effects Through Data Fusion

2013

Self Cite

View full text Add to dashboard Cite

We present a probabilistic data fusion framework that combines multiple computational approaches for drawing relationships between drugs and targets. The approach has special relevance to identifying surprising unintended biological targets of drugs. Comparisons between molecules are made based on 2D topological structural considerations, based on 3D surface characteristics, and based on English descriptions of clinical effects. Similarity computations within each modality were transformed into probability scores. Given a new molecule along with a set of molecules sharing some biological effect, a single score based on comparison to the known set is produced, reecting either 2D similarity, 3D similarity, clinical effects similarity or their combination. The methods were validated within a curated structural pharmacology database (SPDB) and further tested by blind application to data derived from the ChEMBL database. For prediction of off-target effects, 3D-similarity performed best as a single modality, but combining all methods produced performance gains. Striking examples of structurally surprising off-target predictions are presented.

show abstract

IcePick: A Flexible Surface-Based System for Molecular Diversity

Cited by 57 publications

References 31 publications

Finding Key Members in Compound Libraries by Analyzing Networks of Molecules Assembled by Structural Similarity

Finding Key Members in Compound Libraries by Analyzing Networks of Molecules Assembled by Structural Similarity

Quantized Surface Complementarity Diversity (QSCD): A Model Based on Small Molecule−Target Complementarity

Prediction of Off-Target Drug Effects Through Data Fusion

Contact Info

Product

Resources

About