9 papers with code • 1 benchmarks • 1 datasets
We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using a text-like representation of chemical reactions (SMILES) and Natural Language Processing neural network Transformer architecture.
The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions.
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials.
RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions
RetroPrime achieves the Top-1 accuracy of 64. 8% and 51. 4%, when the reaction type is known and unknown, respectively, in the USPTO-50 K dataset.
Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction
Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged.
Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials.
We also improve on existing approaches for a molecular optimisation task and show that Chemformer can optimise on multiple discriminative tasks simultaneously.
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis.