ActiveFusion: Fused Representations Improve Active Learning for Molecular Property Prediction

Active learning provides an efficient strategy for molecular property prediction by iteratively prioritizing compounds for experimental evaluation. However, the effectiveness of active learning pipelines depends strongly on the choice of molecular representation, and systematic understanding of how representation families affect the active learning process in terms of uncertainty and predictive performance remains limited. In this work, we introduce ActiveFusion, a framework for integrating heterogeneous molecular representations within active learning workflows for molecular property prediction. The framework enables systematic evaluation of physicochemical descriptors, molecular fingerprints, learned graph neural network (GNN) representations, and pretrained Transformer-based representations, as well as feature-level fusion strategies that combine complementary chemical information sources. ActiveFusion evaluates models across four molecular property prediction regression tasks. Across datasets, we demonstrate that feature fusion between learned graph representations and physicochemical descriptors consistently improves prediction performance and discovery (average final iteration R2 of 0.71, 0.67, and 0.54 for our overall best representations Chemprop+RDKit, Chemprop, and RDKit, respectively). We show that exploration-driven acquisition strategies enhance scaffold coverage and promote sampling of structurally novel regions of chemical space, and that model-agnostic acquisition of new compounds based on diversity has strong performance. Notably, both pretrained and finetuned Transformer-based embeddings do not consistently outperform physicochemical features or GNN-learned representations in our setting, highlighting the continued relevance of chemically interpretable features and learned features from supervised, task-specific models for active learning applications in molecular property prediction. Overall, ActiveFusion provides a systematic framework for studying representation-acquisition interactions in molecular discovery with representation fusion capabilities. Our study offers practical guidance for designing active learning pipelines that balance prediction accuracy with chemical space exploration.