Traditional molecular string representations, such as SMILES, often pose
challenges for AI-driven molecular design due to their non-sequential depiction
of molecular substructures. To address this issue, we introduce Sequential
Attachment-based Fragment Embedding (SAFE), a novel line notation for chemical
structures. SAFE reimagines SMILES strings as an unordered sequence of
interconnected fragment blocks while maintaining compatibility with existing
SMILES parsers. It streamlines complex generative tasks, including scaffold
decoration, fragment linking, polymer generation, and scaffold hopping, while
facilitating autoregressive generation for fragment-constrained design, thereby
eliminating the need for intricate decoding or graph-based models. We
demonstrate the effectiveness of SAFE by training an 87-million-parameter
GPT2-like model on a dataset containing 1.1 billion SAFE representations.
Through targeted experimentation, we show that our SAFE-GPT model exhibits
versatile and robust optimization performance. SAFE opens up new avenues for
the rapid exploration of chemical space under various constraints, promising
breakthroughs in AI-driven molecular design.Comment: Code, data and models available at:
https://github.com/datamol-io/safe