# Unsupervised Learning of Molecular Embeddings for Enhanced Clustering and Emergent Properties for Chemical Compounds

arXiv: https://arxiv.org/abs/2310.18367 · HTML: https://mlsanity.com/paper/2310.18367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [VAMPnets: Deep learning of molecular kinetics](https://mlsanity.com/paper/1710.06012/) (2017)
- [Keeping it Simple: Language Models can learn Complex Molecular Distributions](https://mlsanity.com/paper/2112.03041/) (2021)
- [ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction](https://mlsanity.com/paper/2007.03196/) (2020)
- [Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive Learning](https://mlsanity.com/paper/2306.04371/) (2023)

## Abstract (arXiv)

The detailed analysis of molecular structures and properties holds great potential for drug development discovery through machine learning. Developing an emergent property in the model to understand molecules would broaden the horizons for development with a new computational tool. We introduce various methods to detect and cluster chemical compounds based on their SMILES data. Our first method, analyzing the graphical structures of chemical compounds using embedding data, employs vector search to meet our threshold value. The results yielded pronounced, concentrated clusters, and the method produced favorable results in querying and understanding the compounds. We also used natural language description embeddings stored in a vector database with GPT3.5, which outperforms the base model. Thus, we introduce a similarity search and clustering algorithm to aid in searching for and interacting with molecules, enhancing efficiency in chemical exploration and enabling future development of emergent properties in molecular property prediction models.