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\paragraph{Model Building}  AZOrange is a machine learning package that supports QSAR model building in a full work flow from descriptor computation to automated model building, validation and selection. It promotes model accuracy by using several high performance machine learning algorithms for efficient data set specific selection of the statistical approach \cite{St_lring_2011}.  Chemistry aware model builder (camb) \cite{Murrell_2015} is an R package for the generation of quantitative models. Its features include descriptor calculation (including 905 two-dimensional and 14 fingerprint type descriptors for small molecules, 13 whole protein sequence descriptors, and 8 types of amino acid descriptors), model generation, ensemble modeling, and visualization (With ggplot2).  eTOXLab \cite{Carri__2015} provides a portable modeling framework embedded in a self-contained virtual machine for easy deployment.  Open3DALIGN \cite{Tosco_2011}, Open3DGrid, and Open3DQSAR \cite{Tosco_2010} are a suite of related tools that aid in developed 3D QSAR models. Open3DALIGN performs unsupervised rigid-body molecular alignment, Open3DGrid generates molecular interaction fields (MIFs) in a variety of formats, and Open3DQSAR builds predictive models from the MIFs of aligned molecules. Calculations can be visualized in real time in PyMOL.  QSAR-tools is a set of Python scripts that use RDKit to model quantitative structure activity relationships from 2D chemical data. It inputs SMILES file of a training set and computes a set of smarts descriptors unique to that set. By using a fingerprint file, it trains a linear model to predict a numerical quantity of interest and is capable of mapping the model onto the compound to produce an image.