Graph-sequence enhanced transformer for template-free prediction of natural product biosynthesis.

Abstract

Natural products (NPs) play a vital role in drug discovery, with many FDA-approved drugs derived from these compounds. Despite their significance, the biosynthetic pathways of NPs remain poorly characterized due to their inherent complexity and the limitations of traditional retrosynthesis methods in predicting such intricate reactions. While template-free machine learning models have demonstrated promise in organic synthesis, their application to biosynthetic pathways is still in its infancy. Addressing this gap, we propose the graph-sequence enhanced transformer (GSETransformer), which leverages both graph structural information and sequential dependencies to achieve superior performance in addressing the complexity of biosynthetic data. When evaluated on benchmark datasets, GSETransformer achieves state-of-the-art performance in single- and multi-step retrosynthesis tasks. These results highlight its effectiveness in computational biosynthesis and its potential to facilitate the design of NP-based therapeutics.