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Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis.

Takashi Sakai¹, Ryusuke Sawada², Otoha Ichinose³

¹Department of Dermatology, Faculty of Medicine, Oita University, Oita, Japan.

Journal of Dermatological Science

|May 25, 2025

Summary

A new machine learning system identified dopamine receptor D2 (DRD2) as a therapeutic target for psoriasis, showing promise for new drug discovery in complex diseases.

Keywords:

artificial intelligence big data dopamine receptor D2 machine learning psoriasis

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Area of Science:

Biomedical Research
Computational Biology
Pharmacology

Background:

Traditional drug discovery faces challenges due to a lack of novel therapeutic targets.
Stagnation in pharmaceutical research necessitates innovative approaches to identify new treatment strategies.

Purpose of the Study:

To develop a machine learning (ML)-based system for predicting therapeutic target molecules for diseases.
To validate the ML system's utility by applying it to identify novel treatment strategies for psoriasis.

Main Methods:

Utilized a large clinical database to calculate reporting odds ratios for disease-preventive drugs.
Analyzed chemical structure databases to identify target proteins associated with preventive drug candidates.
Experimentally validated a predicted therapeutic candidate in a psoriasis mouse model.

Main Results:

ML predictions identified Parkinson's disease drugs as potential psoriasis preventive agents.
Dopamine receptor D2 (DRD2) was highlighted as a key therapeutic target.
DRD2 agonist administration alleviated psoriasis symptoms in mice, downregulating IL-17 and reducing tumor necrosis factor-α.

Conclusions:

The ML system successfully identified DRD2's role in psoriasis, demonstrating its utility for therapeutic target discovery.
This approach has broad potential for uncovering disease mechanisms and targets across various medical conditions.