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Related Experiment Video

Updated: May 21, 2026

Development and Optimization of A Human Hepatocellular Carcinoma Patient-Derived Organoid Model for Potential Target Identification and Drug Discovery

Development and Optimization of A Human Hepatocellular Carcinoma Patient-Derived Organoid Model for Potential Target Identification and Drug Discovery

Published on: August 18, 2023

Discovering TEAD4 modulators for hepatocellular carcinoma: a GAN-enabled generative modelling framework.

Varshni Premnath¹, Ramanathan Karuppasamy¹, Jayakumar Kaliappan²

¹Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Frontiers in Bioinformatics

|May 20, 2026

Summary

Researchers developed a computational method using generative adversarial networks (GANs) and XGBoost to identify potential TEAD4 inhibitors for liver diseases. Cholecalciferol (DB00169) was identified as a promising candidate, demonstrating a scalable strategy for drug discovery.

Keywords:

Hippo-YAP signaling TEAD4 density functional theory generative adversarial networks liver diseases

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Area of Science:

Drug Discovery
Computational Chemistry
Hepatology

Background:

Liver diseases pose a significant global health challenge.
Limited availability of validated small-molecule modulators hinders therapeutic progress.
TEAD4, a key regulator in liver biology, is pharmacologically underexplored due to a scarcity of inhibitors.

Purpose of the Study:

To develop a computational framework for identifying novel TEAD4 modulators.
To expand the chemical space for TEAD4-targeted drug discovery using data augmentation.
To discover potential drug candidates for treating liver diseases.

Main Methods:

Utilized a conditional generative adversarial network (GAN) trained on molecular descriptors for data augmentation.
Employed XGBoost classification for robust activity prediction on augmented datasets.
Screened the DrugBank database and refined hits using structure-based evaluation and toxicity filtering.

Main Results:

Identified DB00169 (cholecalciferol) as a potential TEAD4-binding candidate through quantum chemical analysis.
Molecular dynamics simulations confirmed the stability of the cholecalciferol-TEAD4 complex.
The integrated approach successfully generated a focused set of high-confidence TEAD4 modulator candidates.

Conclusions:

Coupling GAN-based augmentation with XGBoost classification and molecular simulations offers a scalable strategy.
This approach facilitates the identification of biologically meaningful TEAD4 modulators.
The findings support TEAD4-targeted drug discovery for various liver diseases.