Showing 5 open source projects for "linux-kernel-webassembly"

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  • 1
    Charmol

    Charmol

    Program for molecular graphics

    Charmol is a cross-platform GUI/command-line based program for making high-quality pictures of molecular structures, nowadays working on Linux, MacOS and Windows. As output, it produces POV-Ray rendered images or files in VRML format. Charmol is capable of making pictures containing: - from small- to large-size molecules - more molecules together (different settings possible) - orbitals using molden and fchk files (GTO functions up to 'h' angular momentum supported) - surfaces using gaussian cube files (also color-mapped surfaces according to potential) - schematic representation of vibrations using arrows - user-defined arrows representing vectorial properties - measuring gauges - combinations of these features (more orbitals together, orbitals and arrows together etc.) ...
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  • 2

    fluids-sh

    acquire fluid properties from the NIST Chemistry WebBook

    fluid.sh is a shell script which utilized wget to acquire fluid properties from the NIST Chemistry WebBook in a format suitable for further processing with shell scripts or e.g. xmgrace. It supports the full functionality provided by the website! The script takes the same input as command line arguments you need to enter on the web forms. It produces a ASCII text file containing the respective data points in columns headed by a well readable description. The advantage is that you do not...
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  • 3
    ProgPorts is an administrative and easy user interface to hundreds of proteomics, molecular dynamics, and protein crystallography programs. It is a hybrid of the prog/setup system, developed at Johns Hopkins University, and GNU-Darwin ports system.
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  • 4
    PepArML: An unsupervised, model-free, combining peptide identification arbiter for tandem mass spectra via machine learning.
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  • 5
    Tools to build molecular-docking activity prediction models by PLS regression with iterative training and pose-selection. Descriptors include (i) docking score(s), (ii) pharmacophore features, (iii) multi-feature descriptors learned by decision trees.
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