Can we use transformers to accurately predict biodegradation pathways, and what how should we evaluate their predictions?
Read our latest preprint, “Predicting Biodegradation Reactions with Transformers: A Sequence-to-Sequence Approach.”
The integration of machine learning techniques into chemical reaction product prediction has gained significant traction due to the need for predictive methods that address the environmental consequences of chemical substances. This paper approaches to the problem by formulating it as a sequence-to-sequence generation task inspired by natural language processing and other reaction prediction tasks.
By reducing the reliance on expensive manual creation of expert-based rules, this innovative approach can help overcome the limitations of traditional biodegradation prediction methods. This research is a significant step forward in our understanding and prediction of chemical substance behavior.