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SynGFN: learning across chemical space with generative flow-based molecular discovery.

Yuchen Zhu¹, Shuwang Li², Jihong Chen¹

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

Nature Computational Science

|November 13, 2025

Summary

This study introduces SynGFN, a novel artificial intelligence approach for molecular discovery. SynGFN models molecular design as chemical reactions, enabling the creation of diverse, synthesizable, and high-performance molecules for therapeutic targets.

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Area of Science:

Computational chemistry
Artificial intelligence in drug discovery
Molecular modeling

Background:

Artificial intelligence (AI) has accelerated the design-make-test-analyze cycle in molecular discovery.
A key bottleneck remains the compartmentalized approach to computer-aided molecular design and synthesis.
Optimization of the design-make-test-analyze cycle requires integrated computational and synthetic strategies.

Purpose of the Study:

To introduce SynGFN, a novel computational framework for molecular design.
To model molecular design as a cascade of simulated chemical reactions for building molecules from synthesizable components.
To enhance the exploration of chemical space and identification of high-performance molecules.

Main Methods:

SynGFN models molecular design as a sequence of simulated chemical reactions.
It utilizes a hierarchically pretrained policy network for accelerated learning across diverse molecular distributions.
A multifidelity acquisition framework is employed to reduce the cost of reward evaluations.

Main Results:

SynGFN explores chemical spaces up to an order of magnitude larger than existing synthesis-aware generative models.
The model identifies diverse, synthesizable, and high-performance molecules.
SynGFN successfully designed inhibitors for GluN1/GluN3A, a therapeutic target for neuropsychiatric disorders.

Conclusions:

SynGFN represents a significant advancement in synthesis-aware molecular design.
The framework overcomes limitations of compartmentalized approaches in computer-aided molecular design.
SynGFN demonstrates potential for accelerating the discovery of novel therapeutics.