gnina (pronounced NEE-na) is a molecular docking program with integrated support for scoring and optimizing ligands using convolutional neural networks. It is a fork of smina, which is a fork of AutoDock Vina.
Help
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Citation
If you find gnina useful, please cite our paper(s):
Protein–Ligand Scoring with Convolutional Neural Networks (Primary citation)
M Ragoza, J Hochuli, E Idrobo, J Sunseri, DR Koes. J. Chem. Inf. Model, 2017
link arXiv
Ligand pose optimization with atomic grid-based convolutional neural networks M Ragoza, L Turner, DR Koes. Machine Learning for Molecules and Materials NIPS 2017 Workshop, 2017 arXiv
Visualizing convolutional neural network protein-ligand scoring J Hochuli, A Helbling, T Skaist, M Ragoza, DR Koes. Journal of Molecular Graphics and Modelling, 2018 link arXiv
Convolutional neural network scoring and minimization in the D3R 2017 community challenge J Sunseri, JE King, PG Francoeur, DR Koes. Journal of computer-aided molecular design, 2018 link PubMed
Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design PG Francoeur, T Masuda, J Sunseri, A Jia, RB Iovanisci, I Snyder, DR Koes. J. Chem. Inf. Model, 2020 link PubMed Chemrxiv
Docker
A pre-built docker image is available here.
Installation
Ubuntu 20.04
apt-get install build-essential cmake git wget libboost-all-dev libeigen3-dev libgoogle-glog-dev libprotobuf-dev protobuf-compiler libhdf5-dev libatlas-base-dev python3-dev librdkit-dev python3-numpy python3-pip python3-pytest
Follow NVIDIA's instructions to install the latest version of CUDA. Make sure nvcc is in your PATH.
Optionally install cuDNN version 7.85 (>= 8.0 is not yet supported).
Install OpenBabel3
git clone https://github.com/openbabel/openbabel.git
git checkout openbabel-3-1-1
mkdir build
cd build
cmake -DWITH_MAEPARSER=OFF -DWITH_COORDGEN=OFF ..
make
make install
Install gnina
git clone https://github.com/gnina/gnina.git
cd gnina
mkdir build
cd build
cmake ..
make
make install
If you are building for systems with different GPUs (e.g. in a cluster environment), configure with -DCUDA_ARCH_NAME=All.
Note that the cmake build will automatically fetch and install libmolgrid if it is not already installed.
The scripts provided in gnina/scripts have additional python dependencies that must be installed.
Usage
To dock ligand lig.sdf to a binding site on rec.pdb defined by another ligand orig.sdf:
gnina -r rec.pdb -l lig.sdf --autobox_ligand orig.sdf -o docked.sdf.gz
To perform docking with flexible sidechain residues within 3.5 Angstroms of orig.sdf (generally not recommend unless prior knowledge indicates pocket is highly flexible):
gnina -r rec.pdb -l lig.sdf --autobox_ligand orig.sdf --flexdist_ligand orig.sdf --flexdist 3.5 -o flex_docked.sdf.gz
To perform whole protein docking:
gnina -r rec.pdb -l lig.sdf --autobox_ligand rec.pdb -o whole_docked.sdf.gz --exhaustiveness 64
To utilize the default ensemble CNN in the energy minimization during the refinement step of docking (10 times slower than the default rescore option):
gnina -r rec.pdb -l lig.sdf --autobox_ligand orig.sdf --cnn_scoring refinement -o cnn_refined.sdf.gz
To utilize the default ensemble CNN for every step of docking (1000 times slower than the default rescore option):
gnina -r rec.pdb -l lig.sdf --autobox_ligand orig.sdf --cnn_scoring all -o cnn_all.sdf.gz
To utilize all empirical scoring using the Vinardo scoring function:
gnina -r rec.pdb -l lig.sdf --autobox_ligand orig.sdf --scoring vinardo --cnn_scoring none -o vinardo_docked.sdf.gz
To utilize a different CNN during docking (see help for possible options):
gnina -r rec.pdb -l lig.sdf --autobox_ligand orig.sdf --cnn dense -o dense_docked.sdf.gz
To minimize and score ligands ligs.sdf already positioned in a binding site:
gnina -r rec.pdb -l ligs.sdf --minimize -o minimized.sdf.gz
All options:
Input:
-r [ --receptor ] arg rigid part of the receptor
--flex arg flexible side chains, if any (PDBQT)
-l [ --ligand ] arg ligand(s)
--flexres arg flexible side chains specified by comma
separated list of chain:resid
--flexdist_ligand arg Ligand to use for flexdist
--flexdist arg set all side chains within specified
distance to flexdist_ligand to flexible
--flex_limit arg Hard limit for the number of flexible
residues
--flex_max arg Retain at at most the closest flex_max
flexible residues
Search space (required):
--center_x arg X coordinate of the center
--center_y arg Y coordinate of the center
--center_z arg Z coordinate of the center
--size_x arg size in the X dimension (Angstroms)
--size_y arg size in the Y dimension (Angstroms)
--size_z arg size in the Z dimension (Angstroms)
--autobox_ligand arg Ligand to use for autobox
--autobox_add arg Amount of buffer space to add to
auto-generated box (default +4 on all six
sides)
--autobox_extend arg (=1) Expand the autobox if needed to ensure the
input conformation of the ligand being
docked can freely rotate within the box.
--no_lig no ligand; for sampling/minimizing flexible
residues
Scoring and minimization options:
--scoring arg specify alternative built-in scoring
function
--custom_scoring arg custom scoring function file
--custom_atoms arg custom atom type parameters file
--score_only score provided ligand pose
--local_only local search only using autobox (you
probably want to use --minimize)
--minimize energy minimization
--randomize_only generate random poses, attempting to avoid
clashes
--num_mc_steps arg number of monte carlo steps to take in each
chain
--num_mc_saved arg number of top poses saved in each monte
carlo chain
--minimize_iters arg (=0) number iterations of steepest descent;
default scales with rotors and usually isn't
sufficient for convergence
--accurate_line use accurate line search
--simple_ascent use simple gradient ascent
--minimize_early_term Stop minimization before convergence
conditions are fully met.
--minimize_single_full During docking perform a single full
minimization instead of a truncated
pre-evaluate followed by a full.
--approximation arg approximation (linear, spline, or exact) to
use
--factor arg approximation factor: higher results in a
finer-grained approximation
--force_cap arg max allowed force; lower values more gently
minimize clashing structures
--user_grid arg Autodock map file for user grid data based
calculations
--user_grid_lambda arg (=-1) Scales user_grid and functional scoring
--print_terms Print all available terms with default
parameterizations
--print_atom_types Print all available atom types
Convolutional neural net (CNN) scoring:
--cnn_scoring arg (=1) Amount of CNN scoring: none, rescore
(default), refinement, all
--cnn arg built-in model to use, specify
PREFIX_ensemble to evaluate an ensemble of
models starting with PREFIX:
crossdock_default2018 crossdock_default2018_
1 crossdock_default2018_2
crossdock_default2018_3
crossdock_default2018_4 default2017 dense
dense_1 dense_2 dense_3 dense_4
general_default2018 general_default2018_1
general_default2018_2 general_default2018_3
general_default2018_4 redock_default2018
redock_default2018_1 redock_default2018_2
redock_default2018_3 redock_default2018_4
--cnn_model arg caffe cnn model file; if not specified a
default model will be used
--cnn_weights arg caffe cnn weights file (*.caffemodel); if
not specified default weights (trained on
the default model) will be used
--cnn_resolution arg (=0.5) resolution of grids, don't change unless you
really know what you are doing
--cnn_rotation arg (=0) evaluate multiple rotations of pose (max 24)
--cnn_update_min_frame During minimization, recenter coordinate
frame as ligand moves
--cnn_freeze_receptor Don't move the receptor with respect to a
fixed coordinate system
--cnn_mix_emp_force Merge CNN and empirical minus forces
--cnn_mix_emp_energy Merge CNN and empirical energy
--cnn_empirical_weight arg (=1) Weight for scaling and merging empirical
force and energy
--cnn_outputdx Dump .dx files of atom grid gradient.
--cnn_outputxyz Dump .xyz files of atom gradient.
--cnn_xyzprefix arg (=gradient) Prefix for atom gradient .xyz files
--cnn_center_x arg X coordinate of the CNN center
--cnn_center_y arg Y coordinate of the CNN center
--cnn_center_z arg Z coordinate of the CNN center
--cnn_verbose Enable verbose output for CNN debugging
Output:
-o [ --out ] arg output file name, format taken from file
extension
--out_flex arg output file for flexible receptor residues
--log arg optionally, write log file
--atom_terms arg optionally write per-atom interaction term
values
--atom_term_data embedded per-atom interaction terms in
output sd data
--pose_sort_order arg (=0) How to sort docking results: CNNscore
(default), CNNaffinity, Energy
Misc (optional):
--cpu arg the number of CPUs to use (the default is to
try to detect the number of CPUs or, failing
that, use 1)
--seed arg explicit random seed
--exhaustiveness arg (=8) exhaustiveness of the global search (roughly
proportional to time)
--num_modes arg (=9) maximum number of binding modes to generate
--min_rmsd_filter arg (=1) rmsd value used to filter final poses to
remove redundancy
-q [ --quiet ] Suppress output messages
--addH arg automatically add hydrogens in ligands (on
by default)
--stripH arg remove hydrogens from molecule _after_
performing atom typing for efficiency (on by
default)
--device arg (=0) GPU device to use
--no_gpu Disable GPU acceleration, even if available.
Configuration file (optional):
--config arg the above options can be put here
Information (optional):
--help display usage summary
--help_hidden display usage summary with hidden options
--version display program version
CNN Scoring
--cnn_scoring determines at what points of the docking procedure that the CNN scoring function is used.
none- No CNNs used for docking. Uses the specified empirical scoring function throughout.rescore(default) - CNN used for reranking of final poses. Least computationally expensive CNN option.refinement- CNN used to refine poses after Monte Carlo chains and for final ranking of output poses. 10x slower thanrescorewhen using a GPU.all- CNN used as the scoring function throughout the whole procedure. Extremely computationally intensive and not recommended.
The default CNN scoring function is an ensemble of 5 models selected to balance pose prediction performance and runtime: dense, general_default2018_3, dense_3, crossdock_default2018, and redock_default2018. More information on these various models can be found in the papers listed above.
Training
Scripts to aid in training new CNN models can be found at https://github.com/gnina/scripts and sample models at https://github.com/gnina/models.
The DUD-E docked poses used in the original paper can be found here and the CrossDocked2020 set is here.