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Updated: Jul 2, 2025

Spatiotemporal Control of Protein Activity through Optogenetic Allosteric Regulation

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Gaussian Process Regression-Based Near-Infrared d-Luciferin Analogue Design Using Mutation-Controlled Graph-Based

Sung Wook Moon¹, Seung Kyu Min¹

¹Departmet of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea.

Journal of Chemical Information and Modeling

|February 16, 2024

Summary

This study introduces an efficient molecular design method using Gaussian process regression and a graph-based genetic algorithm (GB-GA) for small datasets. The enhanced GB-GA optimizes molecular properties faster and more effectively for drug discovery.

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Area of Science:

Chemical Informatics
Computational Chemistry
Drug Discovery

Background:

Molecular discovery is crucial in chemical informatics, but optimizing molecular properties with machine learning on small datasets remains challenging.
Existing methods struggle with efficient molecular design when limited compound databases are available.

Purpose of the Study:

To develop an efficient molecular design method optimized for small databases.
To enhance optimization capability and convergence speed in molecular design using genetic algorithms.

Main Methods:

A novel molecular design method combining Gaussian process regression and a graph-based genetic algorithm (GB-GA) was developed.
Mutation probability control and parameter reduction were introduced in the GB-GA for improved efficiency.
Active learning and iterative design were employed to generate a target-specific database.

Main Results:

The proposed GB-GA method demonstrated superior efficiency in optimizing toward target properties compared to conventional methods.
The scheme effectively accelerates convergence to optimal solutions, even with small datasets.
Successful design of D-luciferin analogues with near-infrared fluorescence for bioimaging applications was achieved.

Conclusions:

The developed molecular design strategy offers an efficient solution for optimizing molecular properties from small databases.
This approach accelerates drug discovery and enables the design of novel molecules for specific applications like bioimaging.