Gotta be SAFE: A New Framework for Molecular Design
This addresses the problem of inefficient molecular representations for AI applications in chemistry, offering a novel approach that is incremental in improving upon existing SMILES methods.
The paper tackled the challenge of AI-driven molecular design by introducing the SAFE framework, a new line notation that reimagines SMILES strings as sequential fragment blocks, and demonstrated its effectiveness with an 87-million-parameter model trained on 1.1 billion representations, showing versatile optimization performance.
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.