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

High Resolution Image Download MS PowerPoint Slide 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 racer TS ( RA pid C onformer E nsembles with RDK it for T ransition S tates), a method for efficient TS conformer ensemble generation. In this work, we describe the algorithm behind racer TS, which is based on constrained distance geometry. To benchmark the performance of racer TS 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 racer TS 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, racer TS achieves these results with a significant reduction in required wall-time. Our results demonstrate that racer TS is a highly efficient TS conformer ensemble generator, allowing for rapid TS conformer sampling in computational chemistry pipelines. Additionally, racer TS paves the way to create meaningful TS data sets to advance machine learning methods for the discovery of novel and sustainable catalysts.