# READRetro web: A user-friendly platform for predicting plant natural product biosynthesis

## TL;DR

READRetro web is a user-friendly platform that helps researchers predict biosynthesis pathways for plant natural products using machine learning.

## Contribution

The platform makes ML-based retrosynthesis accessible to non-computational researchers through a web interface.

## Key findings

- READRetro web provides high predictive accuracy for natural product retrosynthesis.
- The platform is computationally efficient and offers a responsive user experience.
- It integrates the READRetro model to facilitate NP pathway design for broader research communities.

## Abstract

Natural products (NPs), a fundamental class of bioactive molecules with broad applicability, are valuable sources in pharmaceutical research and drug discovery. Despite their significance, the large-scale production of NPs is often limited by their availability and scalability, requiring alternative approaches such as metabolic engineering or biosynthesis. To identify ideal pathways for the mass production of NPs, deep learning-based retrosynthesis models have been recently developed. Such models accelerate NP discovery; however, these tools are often not easy to use for researchers with a limited computational background, because they require complex environment configurations, command-line interfaces, and substantial computational resources. Here, we introduce READRetro web, a user-friendly web platform that integrates the READRetro machine learning (ML) model for retrosynthesis prediction. Based on modern web technologies, our web platform provides a fast and responsive user experience. READRetro Web bridges the gap between advanced ML-driven retrosynthesis and practical research workflows, making retrosynthesis prediction accessible to a broader range of researchers. Our platform demonstrates high predictive accuracy and computational efficiency, offering well-organized results to facilitate NP retrosynthetic pathway design. READRetro Web is freely accessible via https://readretro.net.