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SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.

Kunyang Sun^1,2,3,4, Dorian Bagni^1,2,3,4, Joseph M Cavanagh^1,2,3,4

¹†Kenneth S. Pitzer Theory Center and Department of Chemistry, ‡Department of Bioengineering, and §Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States.

ACS Central Science

|December 4, 2025

Summary

This summary is machine-generated.

SynLlama, a novel approach using fine-tuned Large Language Models (LLMs), generates practical synthetic pathways for novel molecules. This generative AI tool enhances chemical space exploration by focusing on synthesizable compounds.

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Area of Science:

Artificial Intelligence
Computational Chemistry
Drug Discovery

Background:

Generative machine learning models offer potential for chemical space exploration.
A key limitation is the frequent generation of molecules with challenging synthesis routes.
This hinders practical application in drug discovery and development.

Purpose of the Study:

To develop a novel generative model for creating synthesizable molecules.
To fine-tune Large Language Models (LLMs) for chemical synthesis planning.
To create SynLlama, capable of generating complete synthetic pathways.

Main Methods:

Fine-tuning Meta's Llama3 Large Language Models (LLMs).
Developing SynLlama to generate synthetic pathways using accessible building blocks and reaction templates.
Evaluating performance in forward and bottom-up synthesis planning.

Main Results:

SynLlama effectively generates full synthetic pathways for novel molecules.
The model explores a large synthesizable chemical space with reduced data requirements.
SynLlama demonstrates generalization to unseen, purchasable building blocks.
Strong performance in synthesis planning compared to state-of-the-art methods.

Conclusions:

SynLlama offers a powerful tool for exploring synthesizable chemical space.
The model provides practical synthetic routes, overcoming limitations of previous generative approaches.
SynLlama is valuable for pharmaceutical applications, including analog synthesis and hit expansion.