FARM: Functional Group-Aware Representations for Small Molecules
| 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 |
| Description not available. |