Quantum Trail Fields for Molecular Property Prediction: Empirical Validation of a Physics-Inspired Featurization Approach

We present an empirical validation of Quantum Trail Fields (QTF) as a novel molecular featurization method for property prediction. Building on theoretical work that conceptualizes electron behavior as leaving geometric "trails" through molecular space, we develop a computational framework that extracts physics-informed features from 3D molecular coordinates. Through controlled experiments on synthetic molecules, we address five research questions regarding QTF's viability, statistical robustness, and information density. Our results demonstrate that QTF-style features achieve R 2 = 0.588 (95% CI: [−0.315, 0.830]) on solubility prediction with only 42 training samples, providing evidence that quantum-mechanical intuitions can inform molecular machine learning. We identify and correct methodological biases (padding effects), establish reproducible protocols, and demonstrate stable generalization. While this work uses synthetic data for proof-of-concept validation, it establishes the technical foundation for applying QTF to real drug discovery challenges.