Metadata-Version: 1.0
Name: ciipromol
Version: 0.1.0
Summary: Chemical In-vitro, in-vivo profiling.
Home-page: UNKNOWN
Author: Daniel P. Russo
Author-email: russodanielp@gmail.com
License: LICENSE.txt
Description: # CIIProMol
        ## ciipro.rutgers.edu
        
        ### Outline - 
        
        1. From a set of SMILES
        	* obtain CIDS 
        	* Get bioassays for each compound
        
        2. Concatenate bioassays into a bioprofile
        	* eliminate bioassays based of number of actives
        	* remove invivo assays
        	* remove highly correlated assays
        
        3. Find invitro/invivo correlations
        	* remove assays based on different stats
        	* use those stats to find bio nearest neighbors 
        	* find the assays that minimize the difference in activity between target cmp and nn
        
        
        A python class for extending [CIIPro](ciipro.rutgers.edu) functionality.
        CIIProMol looks to extend the [Python rdkit API]
        (http://www.rdkit.org/docs/api/index.html).
        CIIProMol requires several packages (e.g., rdkit, Pandas, Numpy). 
        These dependencies are listed in the file `explicit-spec-file.txt` and 
        can be loaded directly into a new [conda](http://conda.pydata.org/docs/) environment.
        
        Use the following code to install the required Python packages into a 
        new conda environment.
        
        ```bash
        $ conda create --name ciipromol --file explicit-spec-file.txt
        ```
        
        CIIProMol is broken down into three modules:  
        1) `ciipromol.py`  
        2) `ciiprofiler.py`  
        3) `ciipredictor.py`  
        
        ### ciipromol
        
        Contains `class CIIProMol`.  A `CIIProMol` object can by instantiated 
        with an rdkit `Mol` object.  
        
        ```python 
        from ciipromol import *
        from rdkit import Chem
        
        mol = Chem.MolFromSmiles('CC(=O)OC1=CC=CC=C1C(=O)O')
        cpm = CIIProMol(mol, activity=20.0)
        ```
        
        If `cids=None` (default), all PubChem Compound ID's (CIDs) associated with
        that structure will be retrieved.  Biological assays associated with those CIDs
        can be retrieved.
        
        ```python
        cpm.GetBioAssays()
        ```
        
        If `attribute=True` (default), the BioAssays can be acccessed as a Pandas DataFrame
        using the attribute, `cpm.BioAssays`
        
        ### ciiprofiler 
        
        Contains `class CIIProfiler`.  A `CIIProfiler` object can be instantiated by passing 
        a list of `CIIProMol` objects. 
        
         ```python
         ciiprofiler = CIIProfiler([cpm1, cpm2, cmp3])
         ```
         
         The function `MakeBioProfiler()` will concatenate the attributes `cpm1.BioAssays`,
          `cpm2.BioAssays`, etc., into one matrix.  
Platform: UNKNOWN
