# Language Models for Predicting Organic Synthesis Procedures
## 4 The Data
### PRP-931k Dataset Overview  
A separate subset of 1000 instances has been reserved to serve as a gold standard, with each instance manually inspected. The expert corrected all naming and standardization mistakes and removed a few instances with missing key reactant inputs. The gold standard subset will be used as a second testing dataset. The main goal of utilizing such a dataset is to simulate a real-world scenario where the inputs are curated by a human. The 1000 instances are always kept separate and therefore we consider the remaining 931,350 instances as the main dataset for our task. The dataset (931,350 instances) has been split into training (909,665 instances), validation (2000 instances), and testing (20,000 instances) subsets. **Detailed PRP-931k dataset input analysis. ** The input character sequences vary in range from 15 to 256 characters in SMILES notation. The average input lengths were consistent across subsets: training (92.68 characters), validation (93.49 characters), and testing (92.79 characters). In total, the dataset has reactions involving single reactants (388,086 instances), two reactants (502,070 instances), and three reactants (400,057 instances). Reactions with four or more reactants are rare, and the maximum number of reactants is six. To clarify, single reactant reactions can include halogen exchange [52], reduction, oxidation, deacetylation [53], dehalogenation, and others that require either common additional agents or inorganic compounds, which are included in the procedure description (output). Most inputs contain a single reaction product (919,690 instances) while some contain two (11,599 instances). A convenient way of inspecting the dataset reactions is to analyze a \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Action Names** & \\ \hline _Add, MakeSolution_ & Compound name variants were standardized. \\ \hline _Degas, DrySolution, Extract, Partition, Purify, PH, Quench, Recrystallize, Triturate, and Wash_ & Names of solvents have been converted to consistent formats. \\ \hline _Heat, Add, SetTemperature_ & Temperature notations were converted to Celsius. \\ \hline _Heat, Wait, Reflux_ & Duration notations were converted to a machine-readable format. we have used “min” for minutes, “n” for hours, “d” for days and “week” for weeks. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of standardization processes for different actions. TMAP [54] generated by combining our dataset with the Schneider 50k dataset [49], which contains 50,000 annotated reactions that are distributed evenly over 50 reaction classes (10 super-classes). A TMAP generated from DRFP fingerprints [55] displays reactions grouped according to their super-class (Figure 1). All colors other than green indicate the super-class, while green indicates instances from our dataset. Green instances are distributed to cover most spaces where the annotated reactions are present. Additionally, several regions are exclusively green, indicating distinct groups that are not present in the 50k dataset. Throughout the training process, the models are exposed to a diverse range of reactions, crucial for ensuring versatility in their predictive capabilities. Figure 1 demonstrates the coverage of the PRP-931k dataset, presenting reaction classes such as acidylation, deprotection, alkylation and aylation, protection, reduction, carbon-carbon bond formation, oxidation, functional group addition, and functional group interconversion. These categories represent fundamental processes in organic synthesis. Notably, the green regions in the TMAP highlight reactions unique to our dataset, suggesting its capacity to generalize to less-represented reaction types. By including such diversity, the dataset ensures reliable training across both common transformations and specialized pathways, enhancing the predictive power of the models. Although a more detailed examination is possible, such an analysis is beyond the scope of the broad overview provided in this paper for our deep-learning task.