Stefan P. Schmid, Henrik Seng, Thibault Kläy, and Kjell Jorner (2026)
Highlighted by Jan Jensen
Here's another example of a tool that has made research life in the group significantly easier. racerTS takes a single TS structure and generates an ensemble of TS conformers. While this could already be done with CREST or GOAT, those methods were too expensive for the workflows we are developing in the group, so we basically ignored the problem. For minima, we used RDKit for conformational sampling, and we needed something similar for TSs—and that is exactly what racerTS provides.
racerTS is an RDKit-based pipeline for rapidly generating transition-state conformer ensembles using constrained distance geometry. It requires an XYZ structure of a TS guess, the total molecular charge, and the atom indices defining the reaction center; reactant or product SMILES may optionally be supplied. The coordinates and charge are converted into an RDKit Mol object. The reaction-center atoms and their automatically identified nearest neighbors collectively define the “frozen atoms.” ETKDG then generates conformers by repeatedly sampling a distance-bounds matrix while preserving the reaction-center geometry through positional and distance constraints. The resulting structures are refined with MMFF94, with UFF used as a fallback for unsupported atom types. Duplicate conformers are removed when their heavy-atom RMSD relative to a lower-energy conformer is below 0.125 Å. Structures more than 20 kcal/mol above the force-field minimum are discarded, and the surviving ensemble is written to a multi-structure XYZ file. For improved energetic ranking, the full workflow can additionally optimize the ensemble with GFN-FF while fixing the frozen atoms, rank the conformers using GFN2-xTB single-point energies, and repeat the pruning with a 6 kcal/mol window.
Across 20 diverse reactions, racerTS was approximately four times faster than CREST and 500 times faster than GOAT on a single CPU core. This comparison used GFN2-xTB//GFN-FF for racerTS and CREST, whereas GOAT was run at the more expensive GFN2-xTB level. racerTS, CREST, and GOAT all gave median activation-energy errors below 0.18 kcal/mol, with racerTS yielding an error of 0.17 kcal/mol. The xTB post-optimization step was important for racerTS’s accuracy; omitting it increased the speed but degraded the energy ranking. Overall, racerTS offers accuracy and conformational coverage comparable to those of CREST—though it is somewhat less exhaustive than GOAT—at a substantially lower computational cost, with occasional larger energy outliers for individual reactions.
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