PSearch is a tool to generate 3D ligand-based pharmacophore models and perform virtual screening with them.
pip install psearchpmapper >= 0.3.1
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.
NOTE: On a mac, all the commands may need to be prefixed by "python3 -m" since there are conflicting versions of python installations
- Dataset preparation.
psearch.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.
- 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.
- Database creation.
The script takes as input a tab-separated SMILES file containing SMILES and compound id columns.
psearch.prepare_db -i $PROJECT_DIR/compounds.smi -o $PROJECT_DIR/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.
- Virtual screening.
psearch.scripts.screen_db -d compounds.db -q $PROJECT_DIR/models/ -o $PROJECT_DIR/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
All scripts have -h argument to retrieve descriptions of all available options and arguments.
Alina Kutlushina, Pavel Polishchuk
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
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