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

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Mathematical Framework to Identify Optimal Molecule Based on Virtual Ligand Strategy.

Wataru Matsuoka^1,2,3, Ken Hirose⁴, Ren Yamada⁴

¹Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.

Journal of Chemical Information and Modeling

|June 13, 2025

Summary

This study links virtual ligand (VL) parameters to real molecules, enabling quantitative prediction of optimal ligands for organic chemistry reactions. This computational approach accelerates ligand design for transition metal catalysis.

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Area of Science:

Organic Chemistry
Computational Chemistry
Catalysis

Background:

Ligand engineering is crucial for optimizing transition metal catalysis.
The virtual ligand (VL) approach approximates ligands computationally but lacks interpretability.
Previous VL models offered qualitative predictions, limiting practical application.

Purpose of the Study:

To establish a mathematical framework connecting real molecules to virtual ligand parameters.
To enable rapid and quantitative prediction of optimal ligands for chemical reactions.
To validate the predictive algorithm and discuss its performance.

Main Methods:

Development of a mathematical framework to link molecular properties to VL parameters.
Optimization of the VL model within quantum chemical calculations.
Validation of the prediction algorithm across four distinct chemical reactions.

Main Results:

Successful establishment of a quantitative link between real ligands and VL parameters.
Demonstrated accuracy of the prediction algorithm in identifying optimal ligands.
Identification of the approach's limitations and areas for future improvement.

Conclusions:

The developed framework enhances the interpretability and predictive power of the virtual ligand approach.
This method facilitates faster and more accurate ligand discovery in organic synthesis.
The validated algorithm represents a significant advancement in computational catalyst design.