EASER: Energy-Aware Symbolic Ensemble Readout for Conformer-based Molecular Prediction

Flexible molecules are naturally represented by ensembles of three-dimensional conformers, yet many conformer-based predictors rely on neural readouts whose aggregation mechanisms are difficult to inspect and do not explicitly account for relative conformer energies. We introduce EASER, an Energy-Aware Symbolic Ensemble Readout for molecular conformer ensembles. EASER trains and freezes a SchNet encoder to map each conformer to a fixed-dimensional representation. A cross-fitted descriptor model captures readily predictable two-dimensional signal, while energy-conditioned genetic-programming experts construct compact symbolic features to model the remaining residual from conformer embeddings. Their outputs are linearly combined into conformer-level residual scores and aggregated using a learned effective energy scale. Across multiple MoleculeNet benchmarks under both random and scaffold splits, EASER consistently improves over a matched descriptor-only baseline while remaining competitive with established molecular prediction methods. Ablation studies support complementary contributions from descriptor residualization, symbolic feature construction, and energy-conditioned ensemble modeling, while conformer-budget experiments show that most predictive gains can be obtained with relatively small ensembles. At inference, evolutionary search is no longer required: EASER uses only the frozen encoder, descriptor baseline, fixed algebraic expressions, linear coefficients, and explicit energy-aware aggregation. EASER thus provides a compact and auditable readout over learned 3D molecular representations.