FARM: Functional Group-Aware Representations for Small Molecules

Bibliographic Details
Title: FARM: Functional Group-Aware Representations for Small Molecules
Authors: Nguyen, Thao, Huang, Kuan-Hao, Liu, Ge, Burke, Martin D., Diao, Ying, Ji, Heng
Publication Year: 2024
Collection: Computer Science
Quantitative Biology
Subject Terms: Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
More Details: 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 the representations. This strategic reduction in tokenization granularity is intentionally aligned with key drivers of functional properties (i.e., functional groups), enhancing the model's understanding of chemical language. By expanding the chemical lexicon, FARM more effectively bridges SMILES and natural language, ultimately advancing the model's capacity to predict molecular properties. FARM also represents molecules from two perspectives: by using masked language modeling to capture atom-level features and by employing graph neural networks to encode the whole molecule topology. By leveraging contrastive learning, FARM 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 10 out of 12 tasks. These results highlight FARM's potential to improve molecular representation learning, with promising applications in drug discovery and pharmaceutical research.
Comment: Preprint
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2410.02082
Accession Number: edsarx.2410.02082
Database: arXiv
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