Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data Sets

Small-molecule conformer generation (SMCG) is an extremely
important
task in both ligand- and structure-based computer-aided drug design,
especially during the hit discovery phase. Recently, a multitude of
artificial intelligence (AI) models tailored for SMCG have emerged.
Despite developers typically furnishing performance evaluation data
upon releasing their AI models, a comprehensive and equitable performance
comparison between AI models and conventional methods is still lacking.
In this study, we curated a new benchmarking data set comprising 3354
high-quality ligand bioactive conformations. Subsequently, we conducted
a systematic assessment of the performance of four widely adopted
traditional methods (i.e., ConfGenX, Conformator, OMEGA, and RDKit
ETKDG) and five AI models (i.e., ConfGF, DMCG, GeoDiff, GeoMol, and
torsional diffusion) in the tasks of reproducing bioactive and low-energy
conformations of small molecules. In the former task, the AI models
have no advantage, particularly with a maximum ensemble size of 1.
Even the best-performing AI model GeoMol is still worse than any of
the tested traditional methods. Conversely, in the latter task, the
torsional diffusion model shows obvious advantages, surpassing the
best-performing traditional method ConfGenX by 26.09 and 12.97% on
the COV-R and COV-P metrics, respectively. Furthermore, the influence
of force field-based fine-tuning on the quality of the generated conformers
was also discussed. Finally, a user-friendly Web server called fastSMCG
was developed to enable researchers to rapidly and flexibly generate
small-molecule conformers using both traditional and AI methods. We
anticipate that our work will offer valuable practical assistance
to the scientific community in this field.