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Updated: Jan 12, 2026

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

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Transcriptome-conditioned molecule generation via gene interaction-aware fragment modeling with a GPT-based

Bonil Koo^1,2, Bo Kyung Park², Sun Kim^3,4,5,6

¹Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea.

Scientific Reports

|November 4, 2025

Summary

We developed GGIFragGPT, a new AI model that uses gene expression data to design drug molecules. This approach links cellular responses to chemical structures, accelerating phenotype-driven drug discovery.

Keywords:

Fragment-based drug discovery Gene interaction Molecule generation Phenotypic drug discovery Transcriptome Transformer

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Rapid Assembly of Multi-Gene Constructs using Modular Golden Gate Cloning

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Published on: February 5, 2021

Area of Science:

Computational chemistry
Genomics
Drug discovery

Background:

Phenotype-driven drug discovery uses cellular responses to guide therapeutic molecule design.
Transcriptomics provides data on gene expression changes due to chemical stimuli, enabling links between molecular generation and cellular phenotypes.
Challenges exist in connecting transcriptomic data to chemical structure generation due to complex gene interactions and chemical feasibility.

Purpose of the Study:

To develop a novel generative model, GGIFragGPT, that integrates transcriptomic data with gene-gene interaction embeddings for fragment-based molecular generation.
To guide the molecular generation process using biologically relevant genes highlighted by cross-attention mechanisms.
To generate chemically feasible, novel, and diverse molecules aligned with biological context.

Main Methods:

Developed GGIFragGPT, a generative model using an autoregressive transformer architecture.
Integrated transcriptomic perturbation profiles with biologically informed gene-gene interaction embeddings.
Employed cross-attention mechanisms to link gene relevance to molecular generation.

Main Results:

GGIFragGPT generated chemically feasible, novel, and diverse molecules.
Generated compounds were aligned with the biological context indicated by transcriptomic data.
Gene-level interpretability analysis identified key target genes, validating the model's biological relevance.
Case studies demonstrated the generation of plausible inhibitors, such as for CDK7.

Conclusions:

The integration of biological insights, particularly transcriptomic data, into chemical generation processes is promising for phenotype-driven drug discovery.
GGIFragGPT offers a novel approach to link gene expression profiles to the design of targeted therapeutic molecules.
This work highlights the potential of AI in accelerating the discovery of novel drug candidates based on cellular phenotypes.