Atom-Atom-Path similarity and Sphere Exclusion clustering: tools for prioritizing fragment hits

Gobbi, A.; Giannetti, Anthony M.; Chen, Huifen; Lee, Man-Ling

doi:10.1186/s13321-015-0056-8

Cited by 12 publications

(13 citation statements)

References 26 publications

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“…The MAP4K4 dataset for which both affinity predictions and pose predictions were required is shown in Figure 3. It contains a relatively diverse distribution of chemical groups, some of them already described in literature [25][26][27][28]. Figure S2).…”

Section: Introductionmentioning

confidence: 97%

Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets

Selwa

Martiny

Iorga

2016

J Comput Aided Mol Des

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The D3R Grand Challenge 2015 was focused on two protein targets: Heat Shock Protein 90 (HSP90) and Mitogen-Activated Protein Kinase Kinase Kinase Kinase 4 (MAP4K4). We used a protocol involving a preliminary analysis of the available data in PDB and PubChem BioAssay, and then a docking/scoring step using more computationally demanding parameters that were required to provide more reliable predictions. We could evidence that different docking software and scoring functions can behave differently on individual ligand datasets, and that the flexibility of specific binding site residues is a crucial element to provide good predictions.

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Section: Introductionmentioning

confidence: 97%

Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets

Selwa

Martiny

Iorga

2016

J Comput Aided Mol Des

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“…Currently, methods such as k -means, hierarchical cluster analysis (HCA) and neural networks (to name but a few) are frequently mentioned in the scientific literature concerning drug design. HCA techniques have also been incorporated into several computational tools for a quite a long time now regarding statistical analysis and are still the subject of many recent computational applications in chemistry [ 8 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity

et al. 2016

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BackgroundHierarchical cluster analysis (HCA) is a widely used classificatory technique in many areas of scientific knowledge. Applications usually yield a dendrogram from an HCA run over a given data set, using a grouping algorithm and a similarity measure. However, even when such parameters are fixed, ties in proximity (i.e. two equidistant clusters from a third one) may produce several different dendrograms, having different possible clustering patterns (different classifications). This situation is usually disregarded and conclusions are based on a single result, leading to questions concerning the permanence of clusters in all the resulting dendrograms; this happens, for example, when using HCA for grouping molecular descriptors to select that less similar ones in QSAR studies.ResultsRepresenting dendrograms in graph theoretical terms allowed us to introduce four measures of cluster frequency in a canonical way, and use them to calculate cluster frequencies over the set of all possible dendrograms, taking all ties in proximity into account. A toy example of well separated clusters was used, as well as a set of 1666 molecular descriptors calculated for a group of molecules having hepatotoxic activity to show how our functions may be used for studying the effect of ties in HCA analysis. Such functions were not restricted to the tie case; the possibility of using them to derive cluster stability measurements on arbitrary sets of dendrograms having the same leaves is discussed, e.g. dendrograms from variations of HCA parameters. It was found that ties occurred frequently, some yielding tens of thousands of dendrograms, even for small data sets.ConclusionsOur approach was able to detect trends in clustering patterns by offering a simple way of measuring their frequency, which is often very low. This would imply, that inferences and models based on descriptor classifications (e.g. QSAR) are likely to be biased, thereby requiring an assessment of their reliability. Moreover, any classification of molecular descriptors is likely to be far from unique. Our results highlight the need for evaluating the effect of ties on clustering patterns before classification results can be used accurately.Graphical abstractFour cluster contrast functions identifying statistically sound clusters within dendrograms considering ties in proximity

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“…These command line programs extend a set of previously released command line programs from the Autocorrelator package [5] and include published programs for database access and diversity analysis [6, 7]. The full list of command line programs is given in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…This paper introduces chemalot, a set of command line programs for cheminformatics and structure-based design developed at Genentech that can be used for a wide variety of tasks. These command line programs extend a set of previously released command line programs from the Autocorrelator package [ 5 ] and include published programs for database access and diversity analysis [ 6 , 7 ]. The full list of command line programs is given in Fig.…”

Section: Introductionmentioning

confidence: 99%

chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery

Lee¹,

Aliagas²,

Feng

et al. 2017

J Cheminform

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BackgroundAnalyzing files containing chemical information is at the core of cheminformatics. Each analysis may require a unique workflow. This paper describes the chemalot and chemalot_knime open source packages. Chemalot is a set of command line programs with a wide range of functionalities for cheminformatics. The chemalot_knime package allows command line programs that read and write SD files from stdin and to stdout to be wrapped into KNIME nodes. The combination of chemalot and chemalot_knime not only facilitates the compilation and maintenance of sequences of command line programs but also allows KNIME workflows to take advantage of the compute power of a LINUX cluster.ResultsUse of the command line programs is demonstrated in three different workflow examples: (1) A workflow to create a data file with project-relevant data for structure–activity or property analysis and other type of investigations, (2) The creation of a quantitative structure–property-relationship model using the command line programs via KNIME nodes, and (3) The analysis of strain energy in small molecule ligand conformations from the Protein Data Bank database.ConclusionsThe chemalot and chemalot_knime packages provide lightweight and powerful tools for many tasks in cheminformatics. They are easily integrated with other open source and commercial command line tools and can be combined to build new and even more powerful tools. The chemalot_knime package facilitates the generation and maintenance of user-defined command line workflows, taking advantage of the graphical design capabilities in KNIME.Graphical abstractExample KNIME workflow with chemalot nodes and the corresponding command line pipe Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0228-9) contains supplementary material, which is available to authorized users.

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Atom-Atom-Path similarity and Sphere Exclusion clustering: tools for prioritizing fragment hits

Cited by 12 publications

References 26 publications

Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets

Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets

How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity

chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery

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