Hash Collisions in Molecular Fingerprints: Effects on Property Prediction and Bayesian Optimization
This addresses accuracy issues in computational chemistry for drug discovery, but the improvements are incremental.
The study tackled the problem of hash collisions in molecular fingerprints affecting property prediction and Bayesian optimization, finding that exact fingerprints yield a small but consistent improvement in predictive accuracy on benchmarks, though not in optimization performance.
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.