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Related Experiment Video

Updated: Jul 15, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction.

Mianzhi Liu¹, Fan Xiao², Zhiliang Yu²

¹School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China.

Journal of Chemical Information and Modeling

|July 13, 2026

Summary

RetroMPA enhances retrosynthesis models by integrating chemical knowledge, improving accuracy for drug discovery. This plug-and-play module boosts existing algorithms without retraining, validated by wet-lab experiments.

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Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations

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Published on: April 26, 2024

Area of Science:

Organic Chemistry
Computational Chemistry
Drug Discovery

Background:

Data-driven deep learning models for retrosynthesis lack explicit chemical knowledge integration.
Existing models learn reaction patterns from data but struggle with incorporating prior chemical expertise.

Purpose of the Study:

To introduce RetroMPA, a molecular property-aware module to enhance retrosynthesis predictions.
To provide a model-agnostic framework for integrating chemical knowledge into existing retrosynthesis algorithms.

Main Methods:

RetroMPA acts as a posthoc enhancement module, filtering and optimizing predictions from various algorithms.
It operates at the molecular level using a property-aware latent embedding space.
The framework is designed as a plug-and-play solution, requiring no modifications to original model architectures.

Main Results:

RetroMPA improved top-1 accuracy by an average of 5.50% across eight models on USPTO-50K.
Performance gains of approximately 2.03% were observed on the large-scale USPTO-Full dataset.
Wet-lab experiments validated novel substrate combinations for Suzuki-Miyaura coupling, Bucherer reaction, and Friedel-Crafts acylation.

Conclusions:

RetroMPA effectively injects chemical knowledge into retrosynthesis pipelines, enhancing predictive accuracy.
The module's model-agnostic and plug-and-play nature offers broad applicability in computational chemistry.
Experimental validation suggests RetroMPA's potential beyond data fitting, aiding in novel synthetic route discovery.