SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Although the workflow is very robust, it does produce some outliers due to failures in the conformational search or the TS optimization. In the worst cases, these outliers could display extreme properties such as artificially low or high reaction or activation energies, and, hence, sometimes emerge as the best performing structures. One solution is increasing the exhaustiveness of the conformational sampling, but this also leads to significantly longer computational run-times. Instead, we opted for an outlier detection model based on the computed reaction and activation energies. According to the Bell-Evans-Polanyi relationship [148], the reaction energy is expected to correlate with the activation energy. Therefore, an outlier model based on the 2D scatter plot of these two properties can be used to identify potential outliers. We used the EllipticEnvelope from scikit-learn [174]. The contamination hyperparameter was set to 0.00035 by visual inspection to allow for a considerable deviation from the correlation observed in the reference dataset, while simultaneously capturing clear outliers. This outlier detection scheme is illustrated in Figure S12. Accordingly, we incorporated this outlier detection into the property simulation workflow and assigned an extremely low fitness of  $-10^{4}$  to outlier molecules.
Dataset We constructed a dataset of 60,850 molecules by performing multiple mutations of various SELFIES representations of the parent syn-sesquinorbornene. Specifically, the starting structure was used to produce 20 randomly reordered SMILES strings. The resulting SMILES were converted to SELFIES and 20 random mutations were conducted via STONED-SELFIES [112]. All mutants were subsequently converted to SMILES and checked for the presence of the core motif. Mutations that lead to structures without the core motif were discarded. Additionally, molecules that did not pass the filters were also removed. This procedure was repeated on all unique and valid mutated structures until the final number of molecules was reached. Notably, we used the default mutation settings of STONED-SELFIES [112]. When running the dataset through the property simulation workflow, only
35 molecules either did not run through the property simulation workflow properly or were outliers, corresponding to a success rate of  $99.94\%$ . The results of running the reference structures through the property simulation workflow with respect to both property distributions and computational run time are illustrated in Figure S13.
Figure S13: Simulation results for the reference structures in the developed simulation workflow for the design of chemical reaction substrates benchmark set. Left: Histogram of the simulation workflow run times; right: two-dimensional histograms depicting the property distributions of the provided reference dataset: reaction energy  $\Delta E_{r}$  against activation energy  $\Delta E^{\ddagger}$ .
S6 Model Timing Comparison
All deep generative models (SMILES-VAE, SELFIES-VAE, MoFlow, REINVENT, SMILES-LSTM-HC and SELFIES-LSTM-HC) were trained using the first  $80\%$  of the molecules from the protein ligand design tasks. The remaining  $20\%$  of the dataset was used to assess convergence and perform manual hyperparameter optimization. The corresponding figure in the main text provides four timing benchmark metrics: (1) Training Time with GPU, (2) Sample time for 10,000 molecules, (3) Epoch time without GPU and (4) Epoch time with GPU. For each metric, 5 independent experiments were conducted to obtain both average and standard deviations of the respective values. For the first metric, all the generative models were trained using 24 CPUs (AMD Rome 7532 2.40 GHz 256M cache L3) and a single GPU (Tesla A100). For REINVENT, MoFlow, and the LSTM-HC models, a batch size of 512 was used for training. In contrast, the VAE models performed better in terms of validity and reconstruction with a smaller batch size of 8. For sampling times of 10,000 molecules, we used fully trained deep learning models as obtained after the training time measurements and determined the time required for generating 10,000 molecules in the presence of 24 CPUs (AMD Rome 7532 @ 2.40 GHz 256M cache L3) and a single GPU (Tesla A100). The batch size was 512 during sampling for all deep generative models. For GAs, the molecule sampling time was measured only in the presence of 24 CPUs as both JANUS and GB-GA lack GPU support. Notably, in the models that use SELFIES as a molecular representation, the time needed for translating SELFIES to SMILES was included in the final reported time. For JANUS and GB-GA, this metric was obtained by increasing the population size to 10,000 and by using single generation runs and a dummy fitness function that returns a value of 1 for any molecule. For the single epoch times, we report the time required for training models in the presence of either 24 CPUs (AMD Rome 7532 @ 2.40 GHz 256M cache L3) or 24 CPUs (AMD Rome 7532 @ 2.40 GHz 256M cache L3) with a GPU (Tesla A100). For the deep generative models, a batch size of 512 was used. Notably, for both GB-GA and JANUS, the CPU epoch time corresponds to the total time the models need to precondition the genetic operators based on a dataset of reference structures. Additionally, for both these models, preconditioning was performed by providing 1,000 random SMILES from the reference dataset of the protein ligand design task to the molecular design algorithms. Finally, using the CPU epoch times and the number of epochs needed for training when also using a GPU, we estimated the total CPU training times for all the deep generative models by multiplying these two numbers. Notably, we decided not to estimate the corresponding estimated errors via error propagation as these values are merely rough estimates.