MolVoxel is an Easy-to-Use Molecular Voxelization Tool implemented in Python.
It requires minimal dependencies, so it's very simple to install and use. If you want to use numba version, just install numba additionally.
If there's a feature you need, let me know! I'll do my best to add it.
- Required
- Numpy, SciPy
- Optional
- Numba
- PyTorch, CUDA Available
- RDKit, pymol-open-source
@article{seo2023pharmaconet,
title = {PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling},
author = {Seo, Seonghwan and Kim, Woo Youn},
journal = {arXiv preprint arXiv:2310.00681},
year = {2023},
url = {https://arxiv.org/abs/2310.00681},
}
pip install molvoxel
pip install molvoxel[numba, torch, rdkit] # Optional Dependenciesimport molvoxel
# Default (Resolution: 0.5, dimension: 64, density_type: gaussian, sigma: 0.5, library='numpy')
voxelizer = molvoxel.create_voxelizer()
# Set gaussian sigma = 1.0, spatial dimension = (48, 48, 48) with numba library
voxelizer = molvoxel.create_voxelizer(dimension=48, density_type='gaussian', sigma=1.0, library='numba')
# Set binary density with torch library
voxelizer = molvoxel.create_voxelizer(density_type='binary', library='torch')
# CUDA
voxelizer = molvoxel.create_voxelizer(library='torch', device='cuda')from rdkit import Chem # rdkit is not required packages
import numpy as np
def get_atom_features(atom):
symbol, aromatic = atom.GetSymbol(), atom.GetIsAromatic()
return [symbol == 'C', symbol == 'N', symbol == 'O', symbol == 'S', aromatic]
mol = Chem.SDMolSupplier('./test/10gs/10gs_ligand.sdf')[0]
channels = {'C': 0, 'N': 1, 'O': 2, 'S': 3}
coords = mol.GetConformer().GetPositions() # (V, 3)
center = coords.mean(axis=0) # (3,)
atom_types = np.array([channels[atom.GetSymbol()] for atom in mol.GetAtoms()]) # (V,)
atom_features = np.array([get_atom_features(atom) for atom in mol.GetAtoms()]) # (V, 5)
atom_radius = 1.0 # scalar
image = voxelizer.forward_single(coords, center, atom_radius) # (1, 64, 64, 64)
image = voxelizer.forward_types(coords, center, atom_types, atom_radius) # (4, 64, 64, 64)
image = voxelizer.forward_features(coords, center, atom_features, atom_radius) # (5, 64, 64, 64)# PyTorch is required
import torch
device = 'cuda' # or 'cpu'
coords = torch.FloatTensor(coords).to(device) # (V, 3)
center = torch.FloatTensor(center).to(device) # (3,)
atom_types = torch.LongTensor(atom_types).to(device) # (V,)
atom_features = torch.FloatTensor(atom_features).to(device) # (V, 5)
image = voxelizer.forward_single(coords, center, atom_radius) # (1, 64, 64, 64)
image = voxelizer.forward_types(coords, center, atom_types, atom_radius) # (4, 32, 32, 32)
image = voxelizer.forward_features(coords, center, atom_features, atom_radius) # (5, 32, 32, 32)-
$X \in \mathbb{R}^{N\times3}$ : Coordinates of$N$ atoms -
$R \in \mathbb{R}^N$ : Radii of$N$ atoms -
$F \in \mathbb{R}^{N\times C}$ : Atomic Features of$N$ atoms -$C$ channels.
-
$I \in \mathbb{R}^{D \times H \times W \times C}$ : Output Image with$C$ channels. -
$G \in \mathbb{R}^{D\times H\times W \times 3}$ : 3D Grid of$I$ .
# RDKit is required
from molvoxel.rdkit import AtomTypeGetter, BondTypeGetter, MolPointCloudMaker, MolWrapper
atom_getter = AtomTypeGetter(['C', 'N', 'O', 'S'])
bond_getter = BondTypeGetter.default() # (SINGLE, DOUBLE, TRIPLE, AROMATIC)
pointcloudmaker = MolPointCloudMaker(atom_getter, bond_getter, channel_type='types')
wrapper = MolWrapper(pointcloudmaker, voxelizer, visualizer)
image = wrapper.run(rdmol, center, radii=1.0)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent