Abstract
Abstract
Inferring missing molecules in chemical equations is an important task in chemistry and drug discovery. In fact, the completion of chemical equations with necessary reagents is important for improving existing datasets by detecting missing compounds, making them compatible with deep learning models that require complete information about reactants, products, and reagents in a chemical equation for increased performance. Here, we present a deep learning model to predict missing molecules using a multi-task approach, which can ultimately be viewed as a generalization of the forward reaction prediction and retrosynthesis models, since both can be expressed in terms of incomplete chemical equations. We illustrate that a single trained model, based on the transformer architecture and acting on reaction SMILES strings, can address the prediction of products (forward), precursors (retro) or any other molecule in arbitrary positions such as solvents, catalysts or reagents (completion). Our aim is to assess whether a unified model trained simultaneously on different tasks can effectively leverage diverse knowledge from various prediction tasks within the chemical domain, compared to models trained individually on each application. The multi-task models demonstrate top-1 performance of 72.4%, 16.1%, and 30.5% for the forward, retro, and completion tasks, respectively. For the same model we computed round-trip accuracy of 83.4%. The completion task exhibiting improvements due to the multi-task approach.