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Experimental Determination of Chemical Formula

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Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction

Xiaodan Yin^1,2, Xiaorui Wang¹, Zhenxing Wu¹

¹College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.

Journal of Cheminformatics

|March 2, 2025

Summary

This study introduces Syn-MolOpt, a novel molecular optimization method that prioritizes synthesizability alongside desired properties. Syn-MolOpt effectively generates drug candidates that are both optimized and readily synthesized using functional reaction templates.

Keywords:

Metabolic property optimization Molecular optimization Reaction template Synthetic planning Toxicity optimization

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Area of Science:

Computational Chemistry and Cheminformatics
Drug Discovery and Development
Artificial Intelligence in Chemistry

Background:

Molecular optimization is critical in drug development for enhancing drug candidate properties.
Existing deep-learning methods often neglect molecular synthesizability, leading to impractical compounds.
There is a need for optimization algorithms that balance property improvement with synthetic feasibility.

Purpose of the Study:

To develop a synthesis planning-oriented molecular optimization method that addresses the synthesizability gap.
To introduce Syn-MolOpt, a novel approach leveraging functional reaction templates for property-specific optimization.
To evaluate Syn-MolOpt's performance in multi-property optimization tasks, including toxicity and metabolism.

Main Methods:

Developed a general pipeline for constructing property-specific functional reaction template libraries.
Introduced Syn-MolOpt, which utilizes these templates to guide molecular optimization towards desired properties and synthesizability.
Evaluated Syn-MolOpt on four multi-property optimization tasks (GSK3β-Mutagenicity, GSK3β-hERG, GSK3β-CYP3A4, GSK3β-CYP2C19) against benchmark models.

Main Results:

Syn-MolOpt outperformed three benchmark models (Modof, HierG2G, SynNet) in diverse molecular optimization tasks.
Visualization confirmed the effectiveness of functional reaction templates in guiding optimization and generating synthetic routes.
Syn-MolOpt demonstrated robust performance even with limited scoring accuracy, indicating real-world applicability.

Conclusions:

Syn-MolOpt successfully integrates molecular optimization with synthesis planning, producing optimized yet synthesizable compounds.
The method's ability to design property-specific functional reaction template libraries and provide reference synthesis routes is a key innovation.
Syn-MolOpt offers a valuable and adaptable tool for various molecular optimization challenges in drug discovery.