Investigations into the Efficiency of Computer-Aided Synthesis Planning.

ABSTRACT: The efficiency of machine learning (ML) models is crucial to minimize inference times and reduce the carbon footprints of models deployed in production environments. Current models employed in retrosynthesis to generate a synthesis route from a target molecule to purchasable compounds are prohibitively slow. The model operates in a single-step fashion in a tree search algorithm by predicting reactant molecules given a product molecule as input. In this study, we investigate the ability of alternative transformer architectures, knowledge distillation (KD), and simple hyper-parameter optimization to decrease inference times of the Chemformer model. Initially, we assess the ability of closely related transformer architectures and conclude that these models under-performed when using KD. Addit

SUBMITTER: Hartog PBR 

PROVIDER: S-EPMC11863376 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature