Psoriasis is a common inflammatory skin disease that seriously affects the patient's quality of life. The diagnosis of psoriasis is mainly based on clinical and pathological features, and the assessment depends on the psoriasis area and severity index (PASI). However, there are few reliable and accurate evaluation methods to assess lesion severity and therapeutic effects. This work identified 17 model genes from GEO datasets and established 6 psoriasis evaluation models by LASSO regression, linear regression, and random forest separately. Models were trained and evaluated in different GEO datasets. All 6 models accurately classified psoriatic lesions and non-lesional skin in training and testing data, and showed good AUC. In biologics-treated samples, the model scores were positively correlated with the severity of lesions and negatively correlated with treatment length. Thus, models have the potential to assess the therapeutic effects. In addition, the expression of model genes was examined in keratinocytes, skin of IMQ-induced psoriatic mice, and lesions of psoriasis patients. The RNA and protein levels of model genes increased in cytokine-stimulated keratinocytes and psoriatic lesions as expected. This work provides new methods to assess the lesion severity and therapeutic effects of biologics in psoriasis.

Introduction: Herbal formulations have been used for skin whitening in China for centuries, yet systematic evaluations of their efficacy and underlying mechanisms remain limited. This study aims to identify potential anti‐melanogenic compounds in traditional skin‐whitening herbs using network pharmacology and investigate their mechanisms of action. Methods: The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to screen for anti‐melanogenic compounds and their potential targets. A protein–protein interaction (PPI) network was constructed using Cytoscape 3.7.2, followed by KEGG pathway enrichment analysis. The pharmacological effects were assessed in a 3D human skin model, and in vitro experiments were conducted to explore the molecular mechanisms of the identified compounds. Results: Eight potential skin‐whitening herbs were identified, including Ampelopsis japonica , Nepeta tenuifolia Benth., Scutellaria baicalensis Georgi, Asarum heterotropoides F. Schmidt, Ipomoea nil , Angelica dahurica , Angelica sinensis , and Prunus persica , along with ten active compounds: quercetin, kaempferol, wogonin, beta‐sitosterol, baicalein, stigmasterol, anhydroicaritin, agroclavin, moslosooflavone, and acacetin. Among them, quercetin and acacetin demonstrated significant anti‐melanogenic effects in the 3D skin model by downregulating ET‐1, VEGF, and PTGS2 in keratinocytes. Molecular docking analysis further revealed strong binding affinities of quercetin and acacetin to TLR4, CHUK, and RELA in the NF‐κB pathway, suggesting their role in melanogenesis inhibition via suppression of keratinocyte paracrine signaling. Conclusions: This study highlights the potential of network pharmacology as an effective approach for screening anti‐melanogenic compounds from traditional skin‐whitening herbs. Quercetin and acacetin were identified as key bioactive compounds that inhibit melanogenesis by modulating keratinocyte signaling pathways.