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 full 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 extensive 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.

通过引入序列附加式片段嵌入（SAFE）作为化学结构的新型线型表示方法，我们有效地解决了传统分子字符串表示（如 SMILES）对人工智能驱动的分子设计所带来的挑战，同时简化了复杂的生成任务，并且通过在具有片段约束的设计中进行自回归生成，消除了复杂解码或基于图的模型的需要。通过在包含 11 亿 SAFE 表示的数据集上训练一个类似于 GPT2 的模型，我们展示了 SAFE 的有效性，并证明了我们的 SAFE-GPT 模型具有多样而稳健的优化性能。SAFE 为在各种约束条件下快速探索化学空间打开了新的途径，为人工智能驱动的分子设计带来突破性进展。

Gotta be SAFE: 分子设计的新框架

Gotta be SAFE: A New Framework for Molecular Design

Language modeling has seen impressive progress over the last years, mainly
prompted by the invention of the Transformer architecture, sparking a
revolution in many fields of machine learning, with breakthroughs in chemistry
and biology. In this chapter, we explore how analogies between chemical and
natural language have inspired the use of Transformers to tackle important
bottlenecks in the drug discovery process, such as retrosynthetic planning and
chemical space exploration. The revolution started with models able to perform
particular tasks with a single type of data, like linearised molecular graphs,
which then evolved to include other types of data, like spectra from analytical
instruments, synthesis actions, and human language. A new trend leverages
recent developments in large language models, giving rise to a wave of models
capable of solving generic tasks in chemistry, all facilitated by the
flexibility of natural language. As we continue to explore and harness these
capabilities, we can look forward to a future where machine learning plays an
even more integral role in accelerating scientific discovery.

利用自然语言与化学的类比，本文探索了利用 Transformers 处理药物发现过程中的重要障碍，如后合成规划和化学空间探索，并展示了大型语言模型在化学领域解决通用任务的潜力。