# Hash Collisions in Molecular Fingerprints: Effects on Property Prediction and Bayesian Optimization

arXiv: https://arxiv.org/abs/2511.17078 · HTML: https://mlsanity.com/paper/2511.17078/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [DOCKSTRING: easy molecular docking yields better benchmarks for ligand design](https://mlsanity.com/paper/2110.15486/) (2021)
- [Gaussian Process Molecule Property Prediction with FlowMO](https://mlsanity.com/paper/2010.01118/) (2020)
- [Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning](https://mlsanity.com/paper/2508.06199/) (2025)
- [Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints](https://mlsanity.com/paper/2403.17954/) (2024)

## Built on by (most cited first, 1 of 1)

- [VQ-SAD: Vector Quantized Structure Aware Diffusion For Molecule Generation](https://mlsanity.com/paper/2605.00354/) (2026)

## Abstract (arXiv)

Molecular fingerprinting methods use hash functions to create fixed-length vector representations of molecules. However, hash collisions cause distinct substructures to be represented with the same feature, leading to overestimates in molecular similarity calculations. We investigate whether using exact fingerprints improves accuracy compared to standard compressed fingerprints in molecular property prediction and Bayesian optimization where the underlying predictive model is a Gaussian process. We find that using exact fingerprints yields a small yet consistent improvement in predictive accuracy on five molecular property prediction benchmarks from the DOCKSTRING dataset. However, these gains did not translate to significant improvements in Bayesian optimization performance.