Skip to main content
Join the official 2020 Python Developers SurveyStart the survey!

PSearch: ligand-based pharmacophore modeling and screening

Project description

PSearch - 3D ligand-based pharmacophore modeling

PSearch is a tool to generate 3D ligand-based pharmacophore models and perform virtual screening with them.

Installation

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. Dataset preparation.
prepare_datatset -i $PROJECT_DIR/input.smi -c 4

-i - path to the input file
-c - number of CPUs to use
There are some other arguments which one can use. Invoke script with -h key to get full information.

The script takes as input a tab-separated SMILES file containing SMILES, compound id, activity columns without a header. The third column should contain a word active or inactive. The script splits input compounds on active and inactive subsets, generates stereoisomers and conformers, creates databases of active and inactive compounds with labeled pharmacophore features.

  1. Model building.
psearch -p $PROJECT_DIR -c 4

-p - path to the project dir
-c- number of CPUs to use

There are two other arguments which are worth to mention:
-t - threshold for compound clustering to create training sets. Default: 0.4.
-ts - strategies to create training sets. 1 - a single training set will be created from centroids of individual clusters (capturing a common binding mode for all compounds). 2 - multiple training sets will be created, one per cluster (capturing individual binding modes for compound clusters). By default both strategies are used.

Virtual screening with pharmacophore models

  1. Database creation.

The script takes as input a tab-separated SMILES file containing SMILES and compound id columns.

prepare_db -i compounds.smi -o compounds.db -c 4 -v

-i - path to the input file
-c - number of CPUs to use -v - print progress
There are other arguments available to tweak database generation. To get the full list of arguments invoke -h key.

  1. Virtual screening.
screen_db -d compounds.db -q $PROJECT_DIR/models/ -o screen_results/ -c 4

-d - input generated SQLite database
-q - pharmacophore model or models or a directory with models
If a directory would be specified all pma- and xyz-files will be recognized as pharmacophores and will be used for screening
-o - path to an output directory if multiple models were supplied for screening or a path to a text file
-c- number of CPUs to use

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for psearch, version 0.0.4
Filename, size File type Python version Upload date Hashes
Filename, size psearch-0.0.4-py3-none-any.whl (38.1 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size psearch-0.0.4.tar.gz (27.5 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page