CLMs based on SMILES notation have also been adapted to these practical requirements of the drug design process. For example, Langevin et al. proposed SAMOA that enforced free or constrained sampling from a CLM to conduct scaffold decoration or two-fragment linking i.e., allowing free sampling at a desired decoration or linking attachment point. Alternatively, Arús-Pous et al. proposed an RNN encoder-decoder architecture regarding the task as a sequence-to-sequence translation, for example, translating an input scaffold to the output predicted decorations. This differs from the decoder-only CLMs discussed until this point and requires slicing of a molecular dataset into scaffolds and decorating groups before training the encoder-decoder model. This architecture was used in LibINVENT to generate scaffold-constrained de novo molecules adhering to chemical reaction rules by bespoke dataset slicing via handcrafted reaction rules and reaction filter RL objectives. Succeeding this, the architecture was used in LinkINVENT by instead training the encoder-decoder model to translate a pair of molecular fragments to a linker effectively reversing the translation task. Similarly, SyntaLinker uses a conditional transformer model to translate fragments to fully linked molecules. This approach is impractical as it forces training many different CLMs architectures for each task.