# BERT Learns (and Teaches) Chemistry

arXiv: https://arxiv.org/abs/2007.16012 · HTML: https://mlsanity.com/paper/2007.16012/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Multilingual Molecular Representation Learning via Contrastive Pre-training](https://mlsanity.com/paper/2109.08830/) (2021)
- [Conditional β-VAE for De Novo Molecular Generation](https://mlsanity.com/paper/2205.01592/) (2022)
- [Probing Chemical Language Models: Effects of Pre-training and Fine-tuning](https://mlsanity.com/paper/2607.02140/) (2026)
- [MolFusion: Multimodal Fusion Learning for Molecular Representations via Multi-granularity Views](https://mlsanity.com/paper/2406.18020/) (2024)

## Abstract (arXiv)

Modern computational organic chemistry is becoming increasingly data-driven. There remain a large number of important unsolved problems in this area such as product prediction given reactants, drug discovery, and metric-optimized molecule synthesis, but efforts to solve these problems using machine learning have also increased in recent years. In this work, we propose the use of attention to study functional groups and other property-impacting molecular substructures from a data-driven perspective, using a transformer-based model (BERT) on datasets of string representations of molecules and analyzing the behavior of its attention heads. We then apply the representations of functional groups and atoms learned by the model to tackle problems of toxicity, solubility, drug-likeness, and synthesis accessibility on smaller datasets using the learned representations as features for graph convolution and attention models on the graph structure of molecules, as well as fine-tuning of BERT. Finally, we propose the use of attention visualization as a helpful tool for chemistry practitioners and students to quickly identify important substructures in various chemical properties.