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TransGEM: a molecule generation model based on Transformer with gene expression data.

Yanguang Liu¹, Hailong Yu¹, Xinya Duan¹

¹Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China.

Bioinformatics (Oxford, England)

|April 17, 2024

Summary

This summary is machine-generated.

Artificial intelligence combined with phenotype-based drug design offers a novel approach to discovering new bioactive molecules. The TransGEM model leverages gene expression data to generate molecules with potential therapeutic applications, independent of known disease targets.

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Area of Science:

Computational chemistry and cheminformatics
Artificial intelligence in drug discovery
Genomics and bioinformatics

Background:

Traditional de novo drug design methods (ligand-based and receptor-based) face limitations due to dependency on molecular targets or availability of disease target information.
Phenotype-based de novo drug design offers an alternative strategy, utilizing biological responses rather than specific targets.
Gene expression profiles serve as a rich source of information for characterizing cellular phenotypes.

Purpose of the Study:

To introduce TransGEM (Transformer-based model from gene expression to molecules), a novel phenotype-based de novo drug design model.
To demonstrate the capability of TransGEM in generating new bioactive molecules using gene expression profiles.
To explore the potential of artificial intelligence and phenotype-based approaches in advancing drug discovery.

Main Methods:

Development of TransGEM, a Transformer-based deep learning model tailored for de novo drug design.
Utilizing a specialized gene expression encoder to embed gene expression differences between diseased and normal cells.
Employing attention mechanisms within the Transformer model to identify key genes associated with disease phenotypes.

Main Results:

TransGEM successfully generates molecules with favorable evaluation metrics and property distributions.
Case studies show TransGEM can design structurally novel molecules exhibiting good binding affinity to disease target proteins.
High attention scores highlight genes relevant to disease onset, suggesting potential therapeutic targets.

Conclusions:

The TransGEM model represents a significant advancement in phenotype-based de novo drug design.
This approach enables the generation of novel drug candidates independent of prior knowledge of specific disease targets.
The study establishes a new paradigm for drug discovery, potentially accelerating the identification of treatments for various diseases.