# FARM: Enhancing Molecular Representations with Functional Group Awareness

arXiv: https://arxiv.org/abs/2410.02082 · HTML: https://mlsanity.com/paper/2410.02082/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Pre-training Molecular Graph Representation with 3D Geometry](https://mlsanity.com/paper/2110.07728/) (2021)
- [ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction](https://mlsanity.com/paper/2106.06130/) (2021)
- [Translation between Molecules and Natural Language](https://mlsanity.com/paper/2204.11817/) (2022)
- [Motif-based Graph Self-Supervised Learning for Molecular Property Prediction](https://mlsanity.com/paper/2110.00987/) (2021)
- [Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems](https://mlsanity.com/paper/2307.08423/) (2023)
- [Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs](https://mlsanity.com/paper/2110.01191/) (2021)
- [Chemical-Reaction-Aware Molecule Representation Learning](https://mlsanity.com/paper/2109.09888/) (2021)
- [Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks](https://mlsanity.com/paper/2202.00529/) (2022)
- [L+M-24: Building a Dataset for Language + Molecules @ ACL 2024](https://mlsanity.com/paper/2403.00791/) (2024)
- [SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design](https://mlsanity.com/paper/2307.11694/) (2023)
- [Motif-based Graph Representation Learning with Application to Chemical Molecules](https://mlsanity.com/paper/2208.04529/) (2022)

## Built on by (most cited first, 2 of 2)

- [FGBench: A Dataset and Benchmark for Molecular Property Reasoning at Functional Group-Level in Large Language Models](https://mlsanity.com/paper/2508.01055/) (2025)
- [mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules](https://mlsanity.com/paper/2505.12565/) (2025)

## Abstract (arXiv)

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 idea behind FARM is the incorporation of functional group (FG) annotations at the atomic level, enabling both FG-enhanced SMILES and FG graphs. In this representation, SMILES strings are enriched with functional group information that identifies the group membership of each atom, while the FG graph captures molecular structure by representing how functional groups are connected. This tokenization injects chemical knowledge into SMILES and expands the effective molecular vocabulary, making the representation more suitable for Transformer-based models and more aligned with natural language structure. FARM learns molecular representations from two complementary perspectives to jointly encode functional and structural information. Masked language modeling on FG-enhanced SMILES captures atom-level features enriched with functional context, while graph neural networks model higher-level molecular topology through functional group connectivity. Contrastive learning is then used to align these two views into a unified embedding space, ensuring that both atom-level detail and functional group structure are jointly represented. We evaluate FARM on the MoleculeNet benchmark and achieve state-of-the-art performance on 8 out of 13 tasks. We further validate its generalization ability on a photostability dataset for quantum mechanical properties. These results demonstrate that FARM improves molecular representation learning, supports strong transfer learning across drug discovery and materials science, and enables broad applications in pharmaceutical research and functional material design.