Molecular Representation Matters: Comparative Evaluation of Fingerprints, RDKit Descriptors, and Hashing Effects

Molecular
representations largely determine the learnability of
quantum-chemical properties with machine learning. In order to find
the most appropriate way to represent molecules in chemoinformatic
studies, a comparative study of nine two-dimensional molecular fingerprints
and three RDKit descriptor sets (PHYS, CONF, and PHCO) was conducted
in terms of the prediction of five molecular properties by trained
predictive machine learning models. The use of RDKit descriptor sets
consistently yields more accurate results than hashed fingerprints
across properties. Among fingerprints, Layered Fingerprint outperforms
for global energy targets (Etot, Eee, Exc), whereas
ECFP6 demonstrates better performance for atom-localized (Eatom) and thermodynamic targets (Cp). We further evaluate how the choice of hash function
used during fingerprint construction affects representation quality
and identify that noncryptographic hashing preserves locality and
leads to better and more consistent outcomes than cryptographic hashing
(SHA-256). This work provides mechanistic insights into how different
molecular representations encode structural and physicochemical information,
highlighting the merits and limits of descriptors for learning quantum-chemical
properties. This offers practical guidance for selecting molecular
representations and hashing strategies in designing and establishing
pipelines for the artificial intelligence study of chemistry.