Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials

Computational programs accelerate the chemical discovery
processes
but often need proper three-dimensional molecular information as part
of the input. Getting optimal molecular structures is challenging
because it requires enumerating and optimizing a huge space of stereoisomers
and conformers. We developed the Python-based Auto3D package for generating
the low-energy 3D structures using SMILES as the input. Auto3D is
based on state-of-the-art algorithms and can automatize the isomer
enumeration and duplicate filtering process, 3D building process,
geometry optimization, and ranking process. Tested on 50 molecules
with multiple unspecified stereocenters, Auto3D is guaranteed to find
the stereoconfiguration that yields the lowest-energy conformer. With
Auto3D, we provide an extension of the ANI model. The new model, dubbed
ANI-2xt, is trained on a tautomer-rich data set. ANI-2xt is benchmarked
with DFT methods on geometry optimization and electronic and Gibbs
free energy calculations. Compared with ANI-2x, ANI-2xt provides a
42% error reduction for tautomeric reaction energy calculations when
using the gold-standard coupled-cluster calculation as the reference.
ANI-2xt can accurately predict the energies and is several orders
of magnitude faster than DFT methods.