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EasyDock - Python module to automate molecular docking

Installation

pip install easydock

or the latest version from github

pip install git+https://github.com/ci-lab-cz/easydock.git

Dependencies

conda install -c conda-forge python=3.9 numpy=1.20 rdkit scipy dask distributed

pip install paramiko meeko vina

pip install torch==1.13.1+cpu  --extra-index-url https://download.pytorch.org/whl/cpu
pip install torch-geometric==2.0.1
pip install torch_scatter==2.1.1+pt113cpu -f https://data.pyg.org/whl/torch-1.13.1%2Bcpu.html
pip install torch_sparse==0.6.17+pt113cpu -f https://data.pyg.org/whl/torch-1.13.1%2Bcpu.html
pip install torch_spline_conv==1.2.2+pt113cpu -f https://data.pyg.org/whl/torch-1.13.1%2Bcpu.html
pip install cairosvg svgutils
pip install git+https://github.com/Feriolet/dimorphite_dl.git
pip install git+https://github.com/DrrDom/pkasolver.git

Description

The fully automatic pipeline for molecular docking.

Features:

Pipeline:

Example

Docking from command line

Docking using vina takes input SMILES and a config file. Ligands will not be protonated with Chemaxon, so their supplied charged states will be used. 4 CPU cores will be used (4 molecules will be docked in parallel). When docking will finish an SDF file will be created with top docking poses for each ligand.

run_dock -i input.smi -o output.db --program vina --config config.yml --no_protonation -c 4 --sdf

Example of config.yml for vina docking

protein: /path/to/protein.pdbqt
protein_setup: /path/to/grid.txt
exhaustiveness: 8
seed: 0
n_poses: 5
ncpu: 5

NOTE: ncpu argument in run_dock and config.yml has different meaning. In run_dock it means the number of molecules docked in parallel. In config.yml it means the number of CPUs used for docking of a single molecule. The product of these two values should be equal or a little bit more than the number of CPUs on a computer.

The same but using gnina

run_dock -i input.smi -o output.db --program gnina --config config.yml --no_protonation -c 4 --sdf

Example of config.yml for gnina docking

script_file: /path/to/gnina_executable
protein: /path/to/protein.pdbqt
protein_setup: /path/to/grid.txt
exhaustiveness: 8
scoring: default
cnn_scoring: rescore
cnn: dense_ensemble
n_poses: 10
addH: False
ncpu: 1
seed: 0

To use smina invoke gnina as shown above and make corresponding changes in config.yml

script_file: /path/to/gnina_executable
protein: /path/to/protein.pdbqt
protein_setup: /path/to/grid.txt
exhaustiveness: 8
scoring: vinardo
cnn_scoring: None
cnn: dense_ensemble
n_poses: 10
addH: False
ncpu: 1
seed: 0
Docking using multiple servers

To distribute docking over multiple servers one have to start dask cluster and call the script

dask ssh --hostfile $PBS_NODEFILE --nworkers 15 --nthreads 1 &
sleep 10
run_dock -i input.smi -o output.db --program vina --config config.yml --no_protonation --sdf --hostfile $PBS_NODEFILE --dask_report

$PBS_NODEFILE is a file containing list of IP addresses of servers. The first one from the list will be used by a dask scheduler, but it will also participate in computations.

--nworkers is the number of workers per host. This is the number of molecules which are docked in parallel on a single host.

--nthreads can be any value. The number of CPUs used for docking of a single molecule will be taken from config.yml.

--dask_report argument will create at the end of calculations a html-file with performance report (may be useful to tweak docking parameters).

Important setup issue - the limit of open files on every server should be increased to the level at least twice the total number of requested workers (file streams are used for internode communication by dask).

Data retrieval from the output database

To extract data from the database one may use the script get_sdf_from_dock_db.

Extract top poses with their scores (additional information in DB fields can be extracted only for the top poses):

get_sdf_from_dock_db -i output.db -o output.sdf --fields docking_score

Retrieve second poses for compounds mol_1 and mol_4 in SDF format:

get_sdf_from_dock_db -i output.db -o output.sdf -d mol_1 mol_4 --poses 2

Instead of a list of ids a text file can be supplied as an argument -d.

Retrieve top poses for compounds with docking score less than -10:

get_sdf_from_dock_db -i output.db -o output.sdf --fields docking_score --add_sql 'docking_score < -10'
Docking from Python

Dock a list of molecules on a local computer. Import mol_dock function from a corresponding submodule.

from easydock.run_dock import docking
from easydock.vina_dock import mol_dock
# from easydock.gnina_dock import mol_dock  # <- enable gnina docking
from rdkit import Chem

smiles = ['CC(=O)O', 'NCC(=O)O', 'NC(C)C(=O)O']
mols = [Chem.MolFromSmiles(smi) for smi in smiles]

# assign names, because this will be an identifier of docking outputs of a molecule 
for mol, smi in zip(mols, smiles):
    mol.SetProp('_Name', smi)

for mol_id, res in docking(mols, dock_func=mol_dock, dock_config='config.yml', ncpu=4):
    print(mol_id, res)
Retrieval output poses
  1. Using --sdf option of the main script run_dock will return top poses with docking scores. If there were several enumerated stereoisomers, it will return the pose and the score of the best scoring stereoisomer only.
  2. Using get_sdf_from_dock_db script. it has a rich set of settings and can return SDF as well as SMILES files. The only restriction it cannot currently return the best pose among enumerated stereoisomers. In this case it is advised to use the previous option and invoke run_dock -o database.db --sdf on the database with docked molecules.
Customization

To implement support of a custom docking program one should implement a function like mol_dock which will take as input an RDKit mol object (named molecule) and a yml-file with all docking parameters. The function should run a command line script/utility and return back a tuple of a molecule name and a dictionary of parameters and their values which should be stored in DB (parameter names should be exactly the same as corresponding field names in DB). For examples, please look at mol_dock functions in vina_dock or gnina_dock.

Notes

Protonation notes

pkasolver enumerated protonation states and the closest to pH 7.4 is chosen. In some cases it may return invalid SMILES, e.g. O=C(N1CCN(CC1)C(=O)C=2C=CC=C(C#CC3CC3)C2)C=4NN=C5CCCC45 -> O=C(c1cccc(C#CC2CC2)c1)N1CC[NH](C(=O)c2[nH]nc3c2CCC3)CC1, which will be skipped and a corresponding warning message will appear.

Please note, that protonation states generated with pkasolver were not validated. So, check protonation states.

Multiple CPUs

Please pay attention for --ncpu argument if you use --protonation pkasolver. For ncpu > 1 it may result in some errors. Please report this issue.

Changelog

0.3.1

0.3.0

0.2.9

0.2.8

0.2.7

0.2.6

0.2.5

0.2.4

0.2.3

0.2.2

0.2.1

0.2.0

0.1.2

Licence

BSD-3

Citation

Minibaeva, G.; Ivanova, A.; Polishchuk, P.,
EasyDock: customizable and scalable docking tool.
Journal of Cheminformatics 2023, 15 (1), 102.
https://doi.org/10.1186/s13321-023-00772-2