Path-weighted atom vectors and ChemBERTa fusion for predicting physicochemical properties
TL;DR
Experiments show that PWAV generally improves over classical fingerprint descriptors within learned models and achieves competitive performance relative to established external baselines on several endpoints, positioning PWAV as a competitive and chemically transparent component for hybrid molecular property prediction, rather than as a replacement for domain-specific benchmark systems.
Abstract
Molecular property prediction is central to cheminformatics and environmental chemistry, where accurate modeling of physicochemical properties supports risk assessment and molecular design. Classical descriptors and recent advances such as ChemBERTa have enabled learning chemically contextual representations directly from SMILES, while the integration of structured descriptors with transformer-based embeddings offers a promising pathway toward accurate and interpretable prediction. In this study, we introduce Path-Weighted Atom Vectors (PWAVs), a descriptor family that captures atom-level, environment-aware structural information. We evaluate PWAV both as a standalone representation and in combination with ChemBERTa embeddings through a gated fusion architecture incorporating modality dropout, FiLM conditioning, and auxiliary supervision. Experiments on six physicochemical property datasets ( log P, log S, log BCF, boiling point, melting point, and vapor pressure) show that PWAV generally improves over classical fingerprint descriptors within learned models and achieves competitive performance relative to established external baselines on several endpoints. The strongest gains are observed for boiling point, aqueous solubility, and partition coefficient prediction, where descriptor-embedding fusion yields the best results among the learned models considered. Ablation analyses demonstrate that PWAV contributes complementary structural information beyond SMILES-only ChemBERTa representations, while SHapley Additive exPlanations-based interpretability shows that predictive signal is concentrated within a compact subset of features, enabling an efficient reduced representation (PWAV-64). Nested cross-validation further confirms the robustness of PWAV within the XGBoost framework. Overall, PWAV provides a compact, interpretable, and extensible descriptor framework that integrates effectively with modern representation-learning approaches. These results position PWAV as a competitive and chemically transparent component for hybrid molecular property prediction, rather than as a replacement for domain-specific benchmark systems.