Predicting the three-dimensional conformations of drug-like molecules is central to computational drug discovery. Molecular docking, pharmacophore modeling, shape-based virtual screening, and structure–activity analysis all depend on conformer ensembles, whose quality directly shapes downstream predictions. Current distance geometry methods such as ETKDGv3, implemented in the widely used RDKit cheminformatics library, rely on generic geometric priors and torsional preferences derived from crystallographic databases, limiting coverage and requiring ongoing manual curation. Here, we replace these empirical priors with geometric parameters derived from invariom model compounds computed at a semiempirical quantum-mechanical level of theory. These parameters are precomputed once and reused across target molecules, so no per-molecule quantum-mechanical calculation is required at conformer generation time. Coupling distance geometry with Monte Carlo sampling guided by invariom-based energy terms yields conformational ensembles that are systematically lower in energy than those generated by ETKDGv3, without the need for manually curated torsion rules. Because invariom parameters are computed rather than extracted from experimental data, this approach extends naturally to chemistries underrepresented in crystallographic repositories, providing a scalable route to improved conformer generation for drug discovery.

Co-authors: Janani Durairaj, Torsten Schwede, Birger Dittrich