Reliable and Performant Identification of Low-Energy Conformers in the Gas Phase and Water

Prediction of compound properties
from structure via quantitative
structure–activity relationship and machine-learning approaches
is an important computational chemistry task in small-molecule drug
research. Though many such properties are dependent on three-dimensional
structures or even conformer ensembles, the majority of models are
based on descriptors derived from two-dimensional structures. Here
we present results from a thorough benchmark study of force field,
semiempirical, and density functional methods for the calculation
of conformer energies in the gas phase and water solvation as a foundation
for the correct identification of relevant low-energy conformers.
We find that the tight-binding ansatz GFN-xTB shows the lowest error
metrics and highest correlation to the benchmark PBE0-D3­(BJ)/def2-TZVP
in the gas phase for the computationally fast methods and that in
solvent OPLS3 becomes comparable in performance. MMFF94, AM1, and
DFTB+ perform worse, whereas the performance-optimized but far more
expensive functional PBEh-3c yields energies almost perfectly correlated
to the benchmark and should be used whenever affordable. On the basis
of our findings, we have implemented a reliable and fast protocol
for the identification of low-energy conformers of drug-like molecules
in water that can be used for the quantification of strain energy
and entropy contributions to target binding as well as for the derivation
of conformer-ensemble-dependent molecular descriptors.