# Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting

arXiv: https://arxiv.org/abs/2403.19844 · HTML: https://mlsanity.com/paper/2403.19844/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery](https://mlsanity.com/paper/2002.06053/) (2020)
- [Multi-Kernel LS-SVM Based Bio-Clinical Data Integration: Applications to Ovarian Cancer](https://mlsanity.com/paper/1704.02846/) (2017)

## Abstract (arXiv)

This study introduces a novel approach, combining substruct counting, k-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings. The integrated method generates comprehensive molecular embeddings that enhance discriminative power and information content. Experimental evaluations demonstrate its superiority over traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone, improving chemoinformatics tasks such as drug classification. The proposed method offers a more informative representation of chemical structures, advancing molecular similarity analysis and facilitating applications in molecular design and drug discovery. It presents a promising avenue for molecular structure analysis and design, with significant potential for practical implementation.