Struct2IUPAC -- Transformer-Based Artificial Neural Network for the Conversion Between Chemical Notations

version 2.0 In this version, we have made corrections and improvements. We added Table 1 with the description of models’ accuracy for various beam sizes. We prepared the distribution of the number of name variations (Figure 8) generated by Transformer. We fixed a bug that led to a non-uniform distribution of 100 000 selected molecules from our test set. We prepared a new 100k subset with the uniform distribution. We recalculated the performance on the new 100k dataset for direct and reverse models (Table 1) and the dependence between model accuracy and the length of SMILES (Figure 4) for the uniform test set. Also, we have redrawn Figure 5 to follow the distribution of the new 100k dataset. One can see that the performance on the new test set stays very high (although not absolute) and comparable to algorithmic-based solutions.