Chemplot

About chemplot-feedstockFeedstock license: BSD-3-ClauseHome: license: BSD-3-ClauseSummary: A python library for chemical space visualization.Development: the last decades, Machine Learning (ML) applications have had a great impact on molecular andmaterial science. However, every ML model requires a definition of its applicability domain. Chemplotis a python library for chemical space visualization that allows users to plot the chemical space oftheir datasets. Chemplot contains smart algorithms behind which uses both structural and tailoredsimilarity. Moreover, it is easy to use even for non-experts.Current build statusAll platforms: Current release infoNameDownloadsVersionPlatformsInstalling chemplotInstalling chemplot from the conda-forge channel can be achieved by adding conda-forge to your channels with:conda config --add channels conda-forgeconda config --set channel_priority strictOnce the conda-forge channel has been enabled, chemplot can be installed with conda:or with mamba:It is possible to list all of the versions of chemplot available on your platform with conda:conda search chemplot --channel conda-forgeor with mamba:mamba search chemplot --channel conda-forgeAlternatively, mamba repoquery may provide more information:# Search all versions available on your platform:mamba repoquery search chemplot --channel conda-forge# List packages depending on `chemplot`:mamba repoquery whoneeds chemplot --channel conda-forge# List dependencies of `chemplot`:mamba repoquery depends chemplot --channel conda-forgeAbout conda-forgeconda-forge is a community-led conda channel of installable packages.In order to provide high-quality builds, the process has been automated into theconda-forge GitHub organization. The conda-forge organization contains one repositoryfor each of the installable packages. Such a repository is known as a feedstock.A feedstock is made up of a conda recipe (the instructions on what and how to buildthe package) and the necessary configurations for automatic building using freelyavailable continuous integration services. Thanks to the awesome service provided byAzure, GitHub,CircleCI, AppVeyor,Drone, and TravisCIit is possible to build and upload installable packages to theconda-forge anaconda.orgchannel for Linux, Windows and OSX respectively.To manage the continuous integration and simplify feedstock maintenanceconda-smithy has been developed.Using the conda-forge.yml within this repository,

It is possible to re-render all ofthis feedstock's supporting files (e.g. the CI configuration files) with conda smithy rerender.For more information please check the conda-forge documentation.Terminologyfeedstock - the conda recipe (raw material), supporting scripts and CI configuration.conda-smithy - the tool which helps orchestrate the feedstock.Its primary use is in the construction of the CI .yml filesand simplify the management of many feedstocks.conda-forge - the place where the feedstock and smithy live and work toproduce the finished article (built conda distributions)Updating chemplot-feedstockIf you would like to improve the chemplot recipe or build a newpackage version, please fork this repository and submit a PR. Upon submission,your changes will be run on the appropriate platforms to give the reviewer anopportunity to confirm that the changes result in a successful build. Oncemerged, the recipe will be re-built and uploaded automatically to theconda-forge channel, whereupon the built conda packages will be available foreverybody to install and use from the conda-forge channel.Note that all branches in the conda-forge/chemplot-feedstock areimmediately built and any created packages are uploaded, so PRs should be basedon branches in forks and branches in the main repository should only be used tobuild distinct package versions.In order to produce a uniquely identifiable distribution:If the version of a package is not being increased, please add or increasethe build/number.If the version of a package is being increased, please remember to returnthe build/numberback to 0.Feedstock Maintainers@JacksonBurns@dajtmullaj@mcsorkun

In one word, how would you describe your research?[IMAGE OMITTED. SEE PDF.]Facilitating.The corresponding author of the article Süleyman Er explains how his research group is approaching the challenge of visualizing high-dimensional chemical spaces: A common practice is to simply plot the first two- or three-dimensions of the low-dimensional space and use it as a proxy representation of the high-dimensional space. We developed a new chemical space visualization tool, the ChemPlot, which enables both the basic and advanced users to effectively visualize the chemical space of their molecular datasets in two-dimensions. ChemPlot features structural and tailored similarity methods, together with three different dimensionality reduction methods of PCA, t-SNE, and UMAP. A distinguishing feature of ChemPlot is the application of tailored similarity for the property-sensitive visualization of chemical spaces.What prompted you to investigate this topic/problem?The artificial intelligence (AI) models have been increasingly applied in chemistry and material science for the discovery of new molecules and materials for various applications. Unfortunately, there is no unified model that can be applied on the entire chemical space and the performance of each model may vary substantially across the chemical space of compounds. It's therefore good to know what is the so-called applicability domain of the model within the colossal chemical space. We aimed to develop a chemical space visualizer that can convert the high-dimensional molecular dataset information to the human perception level and aid in assessing the applicability domain of AI models that are designed specifically for the molecular sciences.ChemPlot can be used to verify whether a new molecule is within the applicability domain of the employed AI model. In addition, by visualizing the chemical space of the training and test datasets of the model, one can decide whether the dataset is properly split into train and test sets that are both adequately covering the

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