SELFormer: Molecular Representation Learning via SELFIES Language Models

Understand

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.