String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines. However, traditional string-based representations such as SMILES are often prone to syntactic and semantic errors when produced by generative models. To address these problems, a novel representation, SELF-referencIng Embedded Strings (SELFIES), was proposed that is inherently 100% robust, alongside an accompanying open-source implementation. Since then, we have generalized SELFIES to support a wider range of molecules and semantic constraints and streamlined its underlying grammar. We have implemented this updated representation in subsequent versions of \selfieslib, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of \selfieslib (version 2.1.1) in this manuscript.

翻译：基于字符串的分子表示在化学信息学应用中发挥着关键作用，并且随着深度学习在化学领域的成功，已迅速被纳入机器学习流程。然而，传统的字符串表示（如SMILES）在由生成模型生成时，往往容易出现语法和语义错误。为解决这些问题，一种新颖的表示方法——自参考嵌入字符串（SELFIES）被提出，其具有本质上的100%鲁棒性，并配有相应的开源实现。自此以后，我们对SELFIES进行了泛化，以支持更广泛的分子类型和语义约束，并简化了其底层语法。我们在后续版本的 \selfieslib 中实现了这一更新后的表示，并在设计、效率和功能支持方面取得了重大进展。因此，本文展示了 \selfieslib（版本2.1.1）的当前状态。