AI for drug discovery has been a research hotspot in recent years, and
SMILES-based language models has been increasingly applied in drug molecular
design. However, no work has explored whether and how language models
understand the chemical spatial structure from 1D sequences. In this work, we
pre-train a transformer model on chemical language and fine-tune it toward drug
design objectives, and investigate the correspondence between high-frequency
SMILES substrings and molecular fragments. The results indicate that language
models can understand chemical structures from the perspective of molecular
fragments, and the structural knowledge learned through fine-tuning is
reflected in the high-frequency SMILES substrings generated by the model.

AI 用于药物发现是近年来的研究热点，基于 SMILES 的语言模型越来越多地应用于药物分子设计。本研究探讨了语言模型是否以及如何从 1D 序列中理解化学空间结构。我们在化学语言上对 Transformer 模型进行预训练，并朝向药物设计目标进行微调，研究高频 SMILES 子字符串与分子片段的对应关系。结果表明，语言模型可以从分子片段的角度理解化学结构，并且通过微调学到的结构知识反映在模型生成的高频 SMILES 子字符串中。

基于 LLMs 的药物分子结构的碎片水平理解的实证证据

Empirical Evidence for the Fragment level Understanding on Drug  Molecular Structure of LLMs

Fragment-based drug discovery has been an effective paradigm in early-stage
drug development. An open challenge in this area is designing linkers between
disconnected molecular fragments of interest to obtain chemically-relevant
candidate drug molecules. In this work, we propose DiffLinker, an
E(3)-equivariant 3D-conditional diffusion model for molecular linker design.
Given a set of disconnected fragments, our model places missing atoms in
between and designs a molecule incorporating all the initial fragments. Unlike
previous approaches that are only able to connect pairs of molecular fragments,
our method can link an arbitrary number of fragments. Additionally, the model
automatically determines the number of atoms in the linker and its attachment
points to the input fragments. We demonstrate that DiffLinker outperforms other
methods on the standard datasets generating more diverse and
synthetically-accessible molecules. Besides, we experimentally test our method
in real-world applications, showing that it can successfully generate valid
linkers conditioned on target protein pockets.

本文提出了 DiffLinker 方法，该方法采用 E (3)- 等变 3D - 条件扩散模型来设计分子链接剂，能够连接任意数量的分子碎片，自动确定链接剂中的原子数和其附着点，且在标准数据集上表现优异，能够成功生成与蛋白质袋口相一致的链接剂。