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SELFormer: Molecular Representation Learning via SELFIES Language Models by Yüksel, Atakan; Ulusoy, Erva; Ünlü, Atabey; Doğan, Tunca is a scholarly article available to read on EtoBox.

What is SELFormer: Molecular Representation Learning via SELFIES Language Models about?

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, in

Author

Yüksel, Atakan; Ulusoy, Erva; Ünlü, Atabey; Doğan, Tunca

Published

2023

Language

EN