Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis
This study demonstrates the utility of a novel ML-based system for identifying therapeutic targets, as shown by its successful application in uncovering the role of DRD2 in psoriasis. Beyond psoriasis, this system offers significant potential for exploring pathological mechanisms and discovering therapeutic targets across various diseases.
Authors
- Yutaka Hatano (ORCID: https://orcid.org/0000-0002-1349-755X)
- Takashi Sakai (ORCID: https://orcid.org/0000-0001-7128-3237)
- Yoshihiro Yamanishi (ORCID: https://orcid.org/0000-0003-2279-8773)
- Ryusuke Sawada (ORCID: https://orcid.org/0009-0002-5355-1204)
- Takeshi Terabayashi (ORCID: https://orcid.org/0000-0001-7102-784X)
- Otoha Ichinose
- Toshimasa Ishizaki
Institutions
- Okayama University (JP)
- Oita University (JP)
- Kyushu Institute of Technology (JP)
- Nagoya University (JP)
Publication Details
- Journal
- Journal of Dermatological Science
- Published
- 2025-04-26
- DOI
- https://doi.org/10.1016/j.jdermsci.2025.04.012
- Citations
- 1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.50
Funders
- Japanese Dermatological Association
- LEO Fondet
- Japan Society for the Promotion of Science