Metadata-Version: 2.1
Name: psearch
Version: 0.0.2
Summary: PSearch: ligand-based pharmacophore modeling and screening
Home-page: https://github.com/meddwl/psearch
Author: Alina Kutlushina, Pavel Polishchuk
Author-email: alina.kutlushina@pharminnotech.com, pavel_polishchuk@ukr.net
License: UNKNOWN
Description: # PSearch - 3D ligand-based pharmacophore modeling
        
        PSearch is a tool to automatically generate 3D ligand-based pharmacophore models.
        
        ## Installation
        
        ```bash
        pip install psearch
        ```
        
        ## Dependency
        
        `pmapper >= 0.3.1`
        
        ## Example
        
        ### Creation of ligand-based pharmacophore models
        It is recommended to create an empty dir which would be your `$PROJECT_DIR` and copy an input file to that location.  
        There are two steps of pharmacophore model generation.  
        
        1. Data set preparation. It takes as input a comma-separated SMILES file containing `SMILES`, `compound id`, `activity value`. It splits the input on active and inactive subsets, generates stereoisomers and conformers, creates databases of active and inactive compounds with labeled pharmacophore features.
        ```python
        python3 prepare_datatset.py -i $PROJECT_DIR/input.smi -l 6 -u 8 -c 4
        ```
        `-i` - path to the input file;  
        `-u` - threshold to define active compounds (compounds with `activity value >= threshold` are considered active);  
        `-l` - threshold to define inactive compounds (compounds with `activity value <= threshold` are considered inactive);  
        `-c` - number of CPUs to use.  
        There are other arguments available to tweak data set preparation. To get the full list of arguments run `python3 prepare_datatset.py -h`  
        
        2. Model building.  
        
        ```python
        python3 psearch.py -p $PROJECT_DIR -t 0.4 -c 4
        ```
        `-p` - path to the project dir;  
        `-t` - threshold for compound clustering to create training sets;  
        `-c`- number of CPUs to use
        
        ### Virtual screening with pharmacophore models 
        
        TODO
        
        ## Documentation
        
        All scripts have `-h' argument to retrieve descriptions of all available options and arguments.
        
        ## Authors
        Alina Kutlushina, Pavel Polishchuk
        
        ## Citation
        Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures  
        Alina Kutlushina, Aigul Khakimova, Timur Madzhidov, Pavel Polishchuk  
        *Molecules* **2018**, 23(12), 3094  
        https://doi.org/10.3390/molecules23123094
        
        ## License
        BSD-3 clause
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Requires-Python: >=3.6
Description-Content-Type: text/markdown
