Another way of overcoming the fragile nature of SMILES was explored by Kim et al. . The authors introduced a second Transformer for forward synthesis prediction to check that the retrosynthesis prediction is "cycle-consistent", i.e. if the predicted product by the forward model matches the initial target molecule. Furthermore, the authors introduced a latent variable z ∈ R K to help the model search for a diverse set of reactants . Their model (TiedTransformer) reduces both the rate of invalid SMILES generation with a larger degree of diverse predictions. This improvement came at the cost of performing poorly on the top-k accuracy.