SELFormer：基于SELFIES语言模型的分子表示学习
SELFormer: Molecular Representation Learning via SELFIES Language Models
AI总结：
本文提出SELFormer，一种基于Transformer架构并使用SELFIES符号的化学语言模型，通过预训练和微调在分子性质预测任务上优于基于图学习和SMILES的方法，展示了SELFIES在化学语言建模中的优势。
摘要：
对广阔化学空间进行自动化计算分析对于药物发现和材料科学等众多研究领域至关重要。表示学习技术近年来被用于生成复杂数据的紧凑且信息丰富的数值表达。高效学习分子表示的一种方法是通过自然语言处理（NLP）算法处理基于字符串的化学符号。迄今为止提出的大多数方法使用SMILES符号来实现这一目的；然而，SMILES存在许多与有效性和鲁棒性相关的问题，这可能阻碍模型有效揭示数据中隐藏的知识。在本研究中，我们提出了SELFormer，一种基于Transformer架构的化学语言模型，它使用100%有效、紧凑且表达力强的SELFIES符号作为输入，以学习灵活且高质量的分子表示。SELFormer在200万个类药化合物上进行了预训练，并针对多种分子性质预测任务进行了微调。我们的性能评估表明，SELFormer在预测分子水溶性和药物不良反应方面优于所有竞争方法，包括基于图学习的方法和基于SMILES的化学语言模型。我们还通过降维可视化了SELFormer学习到的分子表示，结果表明即使是预训练模型也能区分具有不同结构性质的分子。我们将SELFormer作为程序化工具共享，并提供了其数据集和预训练模型。总体而言，我们的研究证明了在化学语言建模中使用SELFIES符号的益处，并为设计和发现具有所需特征的新型候选药物开辟了新的可能性。
英文摘要：
Automated computational analysis of the vast chemical space is critical for numerous fields of research such as drug discovery and material science. Representation learning techniques have recently been employed with the primary objective of generating compact and informative numerical expressions of complex data. One approach to efficiently learn molecular representations is processing string-based notations of chemicals via natural language processing (NLP) algorithms. Majority of the methods proposed so far utilize SMILES notations for this purpose; however, SMILES is associated with numerous problems related to validity and robustness, which may prevent the model from effectively uncovering the knowledge hidden in the data. In this study, we propose SELFormer, a transformer architecture-based chemical language model that utilizes a 100% valid, compact and expressive notation, SELFIES, as input, in order to learn flexible and high-quality molecular representations. SELFormer is pre-trained on two million drug-like compounds and fine-tuned for diverse molecular property prediction tasks. Our performance evaluation has revealed that, SELFormer outperforms all competing methods, including graph learning-based approaches and SMILES-based chemical language models, on predicting aqueous solubility of molecules and adverse drug reactions. We also visualized molecular representations learned by SELFormer via dimensionality reduction, which indicated that even the pre-trained model can discriminate molecules with differing structural properties. We shared SELFormer as a programmatic tool, together with its datasets and pre-trained models. Overall, our research demonstrates the benefit of using the SELFIES notations in the context of chemical language modeling and opens up new possibilities for the design and discovery of novel drug candidates with desired features.
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