This study introduces a novel approach, combining substruct counting,
$k$-mers, and Daylight-like fingerprints, to expand the representation of
chemical structures in SMILES strings. The integrated method generates
comprehensive molecular embeddings that enhance discriminative power and
information content. Experimental evaluations demonstrate its superiority over
traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone,
improving chemoinformatics tasks such as drug classification. The proposed
method offers a more informative representation of chemical structures,
advancing molecular similarity analysis and facilitating applications in
molecular design and drug discovery. It presents a promising avenue for
molecular structure analysis and design, with significant potential for
practical implementation.

该研究提出了一种新颖的方法，结合子结构计数，k-mers 和类似 Daylight 的指纹，以扩展 SMILES 字符串中化学结构的表示。该集成方法生成全面的分子嵌入，增强了辨别能力和信息内容。实验评估表明，与传统的 Morgan 指纹，MACCS 和单独的 Daylight 指纹相比，该方法优势明显，改善了药物分类等化学信息学任务。该方法提供了化学结构的更丰富表示，推进了分子相似性分析，并促进了在分子设计和药物发现中的应用。它为分子结构分析和设计提供了有前景的研究方向，并具有重要的实际实施潜力。

利用 k-mer 和基于片段的指纹扩展化学表示的分子指纹

Expanding Chemical Representation with k-mers and Fragment-based  Fingerprints for Molecular Fingerprinting

Complex chemical structures, like drugs, are usually defined by SMILES
strings as a sequence of molecules and bonds. These SMILES strings are used in
different complex machine learning-based drug-related research and
representation works. Escaping from complex representation, in this work, we
pose a single question: What if we treat drug SMILES as conventional sentences
and engage in text classification for drug classification? Our experiments
affirm the possibility with very competitive scores. The study explores the
notion of viewing each atom and bond as sentence components, employing basic
NLP methods to categorize drug types, proving that complex problems can also be
solved with simpler perspectives. The data and code are available here:
this https URL

将药物的 SMILES 字符串视为常规的句子，并使用文本分类方法对药物进行分类，证明了复杂问题也可以通过简单的观点解决。

SMILES 语言下的药物分类：基于药物 SMILES 字符串的文本分类方法

When SMILES have Language: Drug Classification using Text Classification  Methods on Drug SMILES Strings

Molecule generation is a task made very difficult by the complex ways in
which we represent molecules computationally. A common technique used in
molecular generative modeling is to use SMILES strings with recurrent neural
networks built into variational autoencoders - but these suffer from a myriad
of issues: vanishing gradients, long-range forgetting, and invalid molecules.
In this work, we show that by combining recurrent neural networks with
convolutional networks in a hierarchical manner, we are able to both extract
autoregressive information from SMILES strings while maintaining signal and
long-range dependencies. This allows for generations with very high validity
rates on the order of 95% when reconstructing known molecules. We also observe
an average Tanimoto similarity of .6 between test set and reconstructed
molecules, which suggests our method is able to map between SMILES strings and
their learned representations in a more effective way than prior works using
similar methods.

将递归神经网络与卷积网络以层次化方式结合，可以从 SMILES 字符串中提取自回归信息并保持信号和长程依赖关系，从而实现了在重构已知分子时非常高的有效性率达到 95%。同时，我们观察到测试集和重构分子之间的平均 Tanimoto 相似性为 0.6，这表明我们的方法在使用类似方法的先前工作中比较有效地将 SMILES 字符串与其学习表示之间进行映射。