delfta#

Overview#

The DelFTa application is an easy-to-use, open-source toolbox for predicting quantum-mechanical properties of drug-like molecules. Using either ∆-learning (with a GFN2-xTB baseline) or direct-learning (without a baseline), the application accurately approximates DFT reference values (ωB97X-D/def2-SVP). It employs state-of-the-art E(3)-equivariant graph neural networks trained on the QMugs dataset of quantum-mechanical properties, and can predict formation and orbital energies, dipoles, Mulliken partial charges and Wiberg bond orders. See the paper for more details.

Installation#

Pre-requisites#

While the Linux (and Windows, through WSL) installations fully support GPU-acceleration via cudatoolkit, only CPU inference is currently available under Mac OS. We currently support Python 3.7 and 3.8 builds.

Installation via conda#

We recommend and support installation via the conda package manager, and that a fresh environment is created beforehand.

conda install delfta -c delfta -c pytorch -c rusty1s -c conda-forge

First run#

DelFTa requires some additional files (e.g. trained models) before it can be used. Open a python CLI and execute the download script included in the package:

python -c "import runpy; _ = runpy.run_module('delfta.download', run_name='__main__')"

Alternatively, call the _download_required function in the download module:

from delfta.download import _download_required
_download_required()

Installation via Docker#

A CUDA-enabled container can be pulled from DockerHub.

We also provide a Dockerfile for manual builds:

docker build -t delfta .

Attach to the provided container with

docker run -it delfta bash

Quick start#

Here’s a simple example of how to run a calculation for a single molecule created from a SMILES string:

# create an Openbabel molecule
from openbabel.pybel import readstring
mol = readstring("smi", "CCO")

# run the prediction
from delfta.calculator import DelftaCalculator
calc = DelftaCalculator()
preds = calc.predict(mol)

print(preds)
# >> {'E_form': array([-1.2982624], dtype=float32), 'charges': [array([-0.0972612 ,  0.1360395 , -0.35543405,  0.04585792,  0.03582753,
    0.02528055,  0.03844234,  0.01702337,  0.17144795])], 'wbo': [array([1.07290873, 1.1458627 , 0.95775243, 0.9563791 , 0.95719671,
   0.94609926, 0.94542666, 1.1332782 ])], 'E_homo': array([-0.34642667], dtype=float32), 'E_lumo': array([0.18278737], dtype=float32), 'E_gap': array([0.52899516], dtype=float32), 'dipole': array([1.9593117], dtype=float32)}

You can also generate Openbabel molecules by e.g., reading from a file to include atom coordinates. See the Openbabel documentation for more information.

Output#

The following table shows the seven output endpoints for the delfta applications. Note in particular that energies are returned in Hartree, rather than eV.

Property

Key

Unit

Formation energy

E_form

Hartree

Energy of highest occupied molecular orbital

E_homo

Hartree

Energy of lowest unoccupied molecular orbital

E_lumo

Hartree

HOMO-LUMO gap

E_gap

Hartree

Molecular dipole

dipole

Debye

Mulliken partial charges

charges

elementary charge

Wiberg bond orders

wbo

–

Tutorials#

In-depth tutorials can be found in the tutorials subfolder. These include:

  • delta_vs_direct.ipynb: This showcases the basics of how to run the calculator, and compares results using direct- and \(\Delta\)-learning models.

  • calculator_options.ipynb: This dives into the different options you can initialize the calculator class with.

  • training.ipynb: A simple example of how networks can be trained.

Additional models#

Models trained on different training set sizes are available here.

Citation#

If you use this software or parts thereof, please consider citing the following BibTex entry:

@article{atz2021delfta,
   title={Open-source Δ-quantum machine learning for medicinal chemistry},
   DOI={10.33774/chemrxiv-2021-fz6v7},
   journal={ChemRxiv},
   publisher={Cambridge Open Engage},
   author={Atz, Kenneth and Isert, Clemens and Böcker, Markus N. A. and Jiménez-Luna, José and Schneider, Gisbert},
   year={2021}
}