DOI: 10.5281/zenodo.15785976
title: QC Singlepoint Dataset: OpenFF ESP Fragment Conformers v1.0

description: A dataset that contains a diverse set of fragments generated from the Enamine 10K and 50K diversity libraries, the curated ZINC and ChEMBL (eps=78) molecules provided by Riniker and Bleiziffer and the OpenFF Industry Benchmark Season 1 Public molecule set.

The fragments were generated using the Recap decomposition scheme built in to RDKit

For each of the generated fragments a set of up to five conformers were generated by:



generating a set of up to 500 conformers with an RMS cutoff of 0.5 Å using the OpenEye backend of the OpenFF toolkit

applying ELF conformer selection retaining only 5 conformers using the OpenEye backend of the OpenFF toolkit

minimizing each conformer using psi4 and HF/6-31G* until the GAU_LOOSE convergence criteria have been met by geometric.


The expectation is that the electrostatic potential and electric field can be computed using the wavefunction stored in this dataset and using which new charge models can be trained.

See the gnn-charge-models repository, and particularly the data-set-curation directory for further details.



General Information




Date: 2022.01.16

Class: OpenFF Basic Dataset

Purpose: ESP / electric field generation.

Collection: BasicDataset

Name: OpenFF ESP Fragment Conformers v1.0

Number of unique molecules: 49599

Number of records: 63787

Submitter: Simon Boothroyd

Number of unique molecules: 49599

Number of conformers: 65116

Number of conformers (min, mean, max): 1, 1, 5

Molecular weight (min, mean, max): 40.03, 148.66, 310.83

Charges: -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0

Elements: Br, C, Cl, F, H, N, O, P, S

Program: psi4

QC Specifications: spec_1



program: psi4

driver: SinglepointDriver.energy

method: hf  basis: 6-31g*

keywords: {'maxiter': 200, 'scf_properties': ['dipole', 'quadrupole', 'wiberg_lowdin_indices', 'mayer_indices']}

protocols: {'wavefunction': <WavefunctionProtocolEnum.orbitals_and_eigenvalues: 'orbitals_and_eigenvalues'>}

SCF Properties:



dipole

quadrupole

wiberg_lowdin_indices

mayer_indices
description: Manifest



OpenFF-ESP-Fragment-Conformers-v1.0_singlepoint_view.sqlite : QCFractal view of optimization dataset 357

docker_handle_dataset_views.tar.gz : Compressed docker image providing a notebook ready to view and handle the data

README.txt on how to launch the docker image and access the data


Instructions for Dataset Handling

This data is stored a an sqlite file the is directly readable with QCFractal as a dataset view. Because sqlite is a general format, this data may also be accessed in that way, where compressed contents are python picked json strings. To make viewing and handling the data straighforward, a docker image wiith a jupyter notebook entry has been provided.

Load the docker image with:

$ docker load -i docker_handle_dataset_views.tar.gz

A user can run the docker image to spawn the jupyter notebook.



$ mkdir views; mv *sqlite views/$ mkdir outputs$ docker run -p 8888:8888 -v ./views:/workspace/views -v ./outputs:/workspace/outputs docker_handle_dataset_views


The -p flag exposed the port 8888 inside the docker image to the port by the same name externally.The -v flag exposes a directory (in this case ./views, so put your dataset views there) to a directory inside the docker image so that the jupyter notebook and access them. The ./outputs directory provides another shared directory that can be useful to pass output files.If using a M* MAC, the flag --platform=linux/amd64 could be required.

Entering the URL that starts with http://127.0.0.1:8888... in a internet browser should lead to a jupyterlab instance.