FARM: Functional Group-Aware Representations for Small Molecules

• URL: http://arxiv.org/abs/2410.02082v3
• Date: Fri, 01 Aug 2025 22:16:06 GMT
• Title: FARM: Functional Group-Aware Representations for Small Molecules
• Authors: Thao Nguyen, Kuan-Hao Huang, Ge Liu, Martin D. Burke, Ying Diao, Heng Ji,
• Abstract summary: We introduce Functional Group-Aware Representations for Small Molecules (FARM)<n>FARM is a novel model designed to bridge the gap between SMILES, natural language, and molecular graphs.<n>We evaluate FARM on the MoleculeNet dataset, where it achieves state-of-the-art performance on 11 out of 13 tasks.
• Score: 55.281754551202326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Functional Group-Aware Representations for Small Molecules (FARM), a novel foundation model designed to bridge the gap between SMILES, natural language, and molecular graphs. The key innovation of FARM lies in its functional group-aware tokenization, which directly incorporates functional group information into SMILES, enriching SMILES with detailed chemical context. For example, instead of using "O" to represent all oxygen atoms, we use specific tokens like "O_ketone" and "O_hydroxyl" to differentiate oxygen atoms belonging to distinct functional groups. This tokenization expands the chemical lexicon, effectively bridging the gap between SMILES and natural language in terms of vocabulary size, ultimately enhancing the model's ability to predict molecular properties. FARM also represents molecules from two perspectives: by (1) using masked language modeling to capture atom-level features and (2) employing graph neural networks to encode the whole molecule topology. FARM leverages contrastive learning to aligns these two views of representations into a unified molecular embedding. We rigorously evaluate FARM on the MoleculeNet dataset, where it achieves state-of-the-art performance on 11 out of 13 tasks. These results highlight FARM's potential to improve molecular representation learning and demonstrate its strong transfer learning capabilities, paving the way for promising applications in drug discovery and pharmaceutical research.

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