DOI: 10.5281/zenodo.13741197
title: Datasets and geometries for "MORE-Q, Dataset for molecular olfactorial receptor engineering by quantum mechanics"

description: We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the structural and electronic data of non-covalent molecular sensors formed by combining 18 mucin-derived olfactorial receptors with 102 body odor volatilome (BOV) molecules. To have a better understanding of their intra- and inter-molecular interactions, we have performed accurate QM calculations in different stages of the sensor design and, accordingly, MORE-Q splits into three subsets: i) MORE-Q-G1: QM data of 18 receptors and 102 BOV molecules, ii) MORE-Q-G2: QM data of 23, 838 BOV-receptor configurations, and iii) MORE-Q-G3: QM data of 1, 836 BOV-receptor-graphene systems. Each subset involves geometries optimized using GFN2-xTB with D4 dispersion correction and up to 39 physicochemical properties, including global and local properties as well as binding features, all computed at the tightly converged PBE+D3 level of theory. By addressing BOV-receptor-graphene systems from a QM perspective, MORE-Q can serve as a benchmark dataset for state-of-the-art machine learning methods developed to predict binding features. This, in turn, can provide valuable insights for developing the next-generation mucin-derived olfactory receptor sensing devices.

The dataset is provided in 3 HDF5 based files. One can also find here a README file with technical usage details and examples of how to access the information stored in the dataset (see createDF.py). We also offer a Github repository for user guide, see https://github.com/LiC1117/MORE-Q.

For more details, one can refer to the manuscript doi: https://doi.org/10.1038/s41597-025-04616-6
description: We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the structural and electronic data of non-covalent molecular sensors formed by combining 18 mucin-derived olfactorial receptors with 102 body odor volatilome (BOV) molecules. To have a better understanding of their intra- and inter-molecular interactions, we have performed accurate QM calculations in different stages of the sensor design and, accordingly, MORE-Q splits into three subsets: i) MORE-Q-G1: QM data of 18 receptors and 102 BOV molecules, ii) MORE-Q-G2: QM data of 23, 838 BOV-receptor configurations, and iii) MORE-Q-G3: QM data of 1, 836 BOV-receptor-graphene systems. Each subset involves geometries optimized using GFN2-xTB with D4 dispersion correction and up to 39 physicochemical properties, including global and local properties as well as binding features, all computed at the tightly converged PBE+D3 level of theory. By addressing BOV-receptor-graphene systems from a QM perspective, MORE-Q can serve as a benchmark dataset for state-of-the-art machine learning methods developed to predict binding features. This, in turn, can provide valuable insights for developing the next-generation mucin-derived olfactory receptor sensing devices.

 

Updates: 

In the README.txt, in total 12 dataframe files will be produced. The missing one was 'G3_VASP_BD', containing the binding features. 

An extended version MORE-QX now is available. See: 10.5281/zenodo.14720508