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Updated: Jun 18, 2025

A Nonsequencing Approach for the Rapid Detection of RNA Editing

A Nonsequencing Approach for the Rapid Detection of RNA Editing

Published on: April 21, 2022

Retrosynthesis prediction with an iterative string editing model.

Yuqiang Han^1,2, Xiaoyang Xu³, Chang-Yu Hsieh⁴

¹College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.

Nature Communications

|July 30, 2024

Summary

This summary is machine-generated.

This study introduces a novel AI approach for retrosynthesis, reframing it as a molecular string editing task. This method enhances precursor prediction accuracy and diversity in drug discovery.

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In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity

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Area of Science:

Organic Chemistry
Computational Chemistry
Drug Discovery

Background:

Retrosynthesis is vital for drug discovery and organic synthesis.
Current AI methods use token-by-token decoding, leading to suboptimal performance and limited diversity.
Chemical reactions cause local molecular changes, implying overlap between reactants and products.

Purpose of the Study:

To develop an AI model for improved single-step retrosynthesis prediction.
To address the limitations of existing token-by-token decoding methods.
To enhance both the accuracy and diversity of predicted precursor compounds.

Main Methods:

Reframing retrosynthesis as a molecular string editing task.
Utilizing a fragment-based generative editing model with explicit sequence editing operations.
Implementing an inference module with reposition sampling and sequence augmentation.

Main Results:

The proposed model generates high-quality and diverse precursor compounds.
Achieved a top-1 accuracy of 60.8% on the USPTO-50K benchmark dataset.
Demonstrated superior performance compared to existing methods.

Conclusions:

The molecular string editing approach is effective for single-step retrosynthesis.
The developed AI model significantly improves prediction accuracy and diversity.
This work offers a promising advancement for AI-driven drug discovery and organic synthesis.