Most Relevant Publications

A link to the most relevant publications related to IBM RXN for Chemistry

Data-Driven Learning Systems for Chemical Reaction Prediction: An Analysis of Recent Approaches

One of the critical challenges in efficient synthesis route design is the accurate prediction of chemical reactivity. Unlocking it could significantly facilitate chemical synthesis and hence, accelerate the discovery of novel molecules and materials.…

Prediction of Chemical Reaction Yields using Deep Learning

Artificial intelligence is driving one of the most important revolutions in organic chemistry. Multiple platforms, including tools for reaction prediction and synthesis planning based on machine learning, successfully became part of the organic…

Unassisted Noise-Reduction of Chemical Reactions Data Sets

Existing deep learning models applied to reaction prediction in organic chemistry are able to reach extremely high levels of accuracy (> 90% for NLP- based ones1). With no chemical knowledge embedded than the information learnt from reaction…

Unsupervised Attention-Guided Atom-Mapping

Knowing how atoms rearrange during a chemical transformation is fundamental to numerous applications aiming to accelerate organic synthesis and molecular discovery. This labelling is known as atom-mapping and is an NP-hard problem. Current solutions…

Carbohydrate Transformer: Predicting Regio- and Stereoselective Reactions Using Transfer Learning

Organic chemistry is central to society because it enables the synthesis of complex molecules and materials used in all fields of science and technology. The synthetic methods represent a vast body of accumulated knowledge optimally suited for…

API documentation

The IBM RXN for Chemistry services can also be accessed through APIs. A short user guide can be downloaded here.