  Machine learning approaches in drug discovery, as well as in other areas of
the chemical sciences, benefit from curated datasets of physical molecular
properties. However, there is a lack of sufficiently large data collections
that include first-principle quantum chemical information on bioactive
molecules, such as single-point electronic properties, quantum mechanical wave
functions and density-functional theory (DFT) matrices. The open-access QMugs
(Quantum-Mechanical Properties of Drug-like Molecules) dataset fills this void.
The QMugs collection comprises quantum mechanical properties of more than 665k
biologically and pharmacologically relevant molecules extracted from the ChEMBL
database, totaling $\sim$2M conformers. QMugs contains optimized molecular
geometries and thermodynamic data obtained via the semi-empirical method
GFN2-xTB. Atomic and molecular properties (e.g., partial charges, energies, and
rotational constants) are provided on both the GFN2-xTB and on the DFT
($\omega$B97X-D/def2-SVP) levels of theory. QMugs also comprises the respective
quantum mechanical wave functions, including DFT density and orbital matrices,
totaling over 7 terabytes of uncompressed data. This dataset is intended to
facilitate the development of models that learn from molecular data on
different levels of theory while also providing insight into the corresponding
relationships between molecular structure and biological activity.
