# NeuralLoc

This work presents RetroChimera, a retrosynthesis prediction framework that combines a Transformer-based de-novo model, R-SMILES 2, with a graph-neural-network model grounded in reaction templates, NeuralLoc, through a learned, rank-dependent ensembling strategy, so that their complementary strengths are leveraged to perform strongly across both common and rare reaction classes and, in blind tests, to produce disconnections of complex molecules that PhD-level chemists preferred over those from its constituent sub-models, from more established approaches, and even from the test set itself.

This work presents RetroChimera, a retrosynthesis prediction framework that combines a Transformer-based de-novo model, R-SMILES 2, with a graph-neural-network model grounded in reaction templates, NeuralLoc, through a learned, rank-dependent ensembling strategy, so that their complementary strengths are leveraged to perform strongly across both common and rare reaction classes and, in blind tests, to produce disconnections of complex molecules that PhD-level chemists preferred over those from its constituent sub-models, from more established approaches, and even from the test set itself.