Chemistry Before Complexity: Representation-First Molecular Learning

Molecular machine learning has increasingly focused on expressive architectures, yet classical fingerprints remain highly competitive with modern graph, language, and geometric models. We argue that *molecular representation remains a central bottleneck*: if chemically relevant information is not effectively exposed, additional model capacity may not recover it. We introduce two chemically informed molecular fingerprints. **AtomGrid** encodes atomic-scale covalent, electrostatic, and dispersion interactions in a fixed coordinate system of chemical environments, while **GroupGrid** captures functional-scale organization through group multiplicity, topological separation, and charge complementarity. We study them together with ECFP, AtomPair, and MACCS as five complementary molecular views, evaluating each independently and combining only their prediction scores through a lightweight gated fusion model. On ligand-based virtual screening with hard negatives, fixed fingerprints outperform substantially more complex learned encoders, with AtomGrid among the strongest standalone methods and the five-fingerprint fusion achieving the best overall performance. On molecular property prediction, the same fusion achieves the best aggregate performance across six OGB benchmarks, despite the proposed fingerprints being weaker standalone. These results suggest that future molecular learning should place greater emphasis on *chemistry-aware representation learning*, not only on increasing downstream model complexity.