Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry

TL;DR

The paper introduces racerTS, a fast method for generating transition-state conformer ensembles, which is important for catalyst design and machine learning applications.

Contribution

The novel contribution is the development of racerTS, a computationally efficient method for generating transition-state conformer ensembles using constrained distance geometry.

Findings

racerTS generates conformer ensembles with accuracy comparable to CREST and slightly less than GOAT.

racerTS achieves sufficient accuracy in low-energy regions with a median error of 0.17 kcal/mol.

racerTS significantly reduces computational time compared to existing methods.

Abstract

Consideration of
transition-state (TS) conformer ensembles
is required
to accurately model a reaction, and thus plays a key role in computational
catalyst design. While CREST and GOAT are established methods for
TS conformer ensemble generation, the associated computational cost
remains a major bottleneck in computational chemistry pipelines, including
for the generation of large machine learning data sets for catalyst
design. To this end, we present racerTS (RApid Conformer Ensembles with RDKit for Transition States), a method for
efficient TS conformer ensemble generation. In this work, we describe
the algorithm behind racerTS, which is based on constrained
distance geometry. To benchmark the performance of racerTS against CREST and GOAT, we created conformer ensembles for transition
states of 20 diverse reactions. To assess the utility of each conformer
generator in computational chemistry workflows, we optimize selected
low-energy and diverse conformers at the DFT level. We use the generated
conformer ensembles and the results of this pipeline to assess conformer
generators according to the following metrics: computational cost,
exhaustiveness, validity, and accuracy in low-energy regions. Considering
the generated ensembles, we find that racerTS covers the
conformer space similarly to CREST and slightly less comprehensively
than GOAT, while the validity of the DFT-optimized TSs is better and
the accuracy in the low-energy region is sufficient for computational
chemistry applications (median error of 0.17 kcal/mol). Remarkably,
racerTS achieves these results with a significant reduction
in required wall-time. Our results demonstrate that racerTS is a highly efficient TS conformer ensemble generator, allowing
for rapid TS conformer sampling in computational chemistry pipelines.
Additionally, racerTS paves the way to create meaningful
TS data sets to advance machine learning methods for the discovery
of novel and sustainable catalysts.

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