Authors: – Aparna K Sapra (Global Product Sales Leader, Scientific Products)
In brief: dermatology drug discovery is being transformed by three convergent forces — precision medicine that targets specific immune pathways (JAK/TYK2 inhibitors, IL-4/IL-13/IL-17 biologics) rather than broad immunosuppression, AI that compresses target and compound discovery timelines by integrating genomic, transcriptomic, and proteomic datasets, and evidence-based cosmeceuticals that apply the same molecular biology to skin aging. Excelra’s GOSTAR™ database, consolidating SAR data from over 4 million patents and 1 million peer-reviewed journals, provides the compound intelligence layer that AI-driven dermatology discovery depends on.
For decades, dermatology relied largely on broad-spectrum treatments that controlled symptoms without addressing the underlying molecular mechanisms of disease. While effective for many patients, these therapies often produced variable responses and long-term safety concerns. Today, advances in genomics, immunology, artificial intelligence (AI), regenerative medicine, and advanced drug delivery are fundamentally changing this landscape. Researchers can now identify disease-driving pathways with greater precision, enabling the development of targeted therapies and evidence-based skin health innovations. At the same time, pharmaceutical and cosmetic research are converging, with both fields leveraging AI, multi-omics, biomarker discovery, and advanced skin models to accelerate innovation.
Precision medicine is redefining therapeutic dermatology
Chronic inflammatory skin diseases such as atopic dermatitis, psoriasis, vitiligo, hidradenitis suppurativa are now benefiting from therapies designed to intervene at highly specific immune pathways rather than suppress the immune system broadly.
One of the most significant advances has been the emergence of Janus kinase (JAK) inhibitors and TYK2 inhibitors. Drugs such as ruxolitinib, tapinarof, roflumilast, and oral TYK2 inhibitors have demonstrated that selective intracellular signalling inhibition can effectively reduce inflammation while improving safety and tolerability compared with conventional systemic immunosuppressants. The success of biologics such as Dupixent (dupilumab) demonstrated the enormous market potential for targeted dermatology therapies and catalysed renewed investment across both pharmaceutical and biotechnology industries.
The therapeutic target landscape continues to expand rapidly to include OX40/OX40L. IL-23 receptor, IL-31, IRAK, Chemokine receptors, Melanocyte activation pathways, Immune checkpoint regulators and Neuroinflammatory signalling pathways, beyond the established IL-4, IL-13, IL-17, IL-23, and TNF pathways, researchers are actively investigating.
Excelra has direct commercial expertise in dermatology drug repurposing and target intelligence. In 2023, Excelra announced a drug repurposing collaboration with Maruho on dermatological applications — applying GOSTAR™ SAR intelligence and data-driven compound analysis to identify repurposing candidates for skin disease targets. This collaboration directly illustrates the GOSTAR-to-dermatology drug discovery pathway described throughout this blog.
The expanding target landscape described here — from IL-4/IL-13 and IL-17 to OX40/OX40L, IRAK, and neuroinflammatory pathways — is exactly the kind of multi-target intelligence that structured drug target dossiers enable. Excelra’s blog on Drug Target Dossier: Target Intelligence for Data-Driven Drug Discovery explains how multi-source evidence — including SAR data, clinical genetics, and pathway context — is assembled into decision-ready target intelligence for dermatology programs.
Cosmetic dermatology is becoming molecular medicine
The cosmetic industry is also undergoing a scientific revolution. Instead of focusing solely on reducing wrinkles or improving skin hydration, researchers increasingly view aging as a biological process that can potentially be modified. Current research targets several hallmarks of skin aging, including cellular senescence, mitochondrial dysfunction, chronic low-grade inflammation, immune aging, extracellular matrix degradation, skin microbiome imbalance as well as epigenetic aging
This has led to a new generation of evidence-based cosmeceuticals designed to influence skin biology by stimulating collagen synthesis, regulating inflammatory pathways, improving tissue regeneration, and restoring microbial balance using emerging active ingredients such as senolytic compounds, collagen-regulating peptides, copper peptides, growth factors, exosomes, microbiome modulators, neurotransmitter-inhibiting peptides as well as regenerative biologics
The convergence of cosmetic science and pharmaceutical biology that makes evidence-based cosmeceuticals possible depends on the same disease landscape analysis tools used in therapeutic drug discovery. Excelra’s whitepaper on Disease Landscapes: Meaningful Scientific and Strategic Assessments for Drug Discovery and Development describes how structured disease landscape assessments — mapping targets, mechanisms, competitive compounds, and clinical evidence — can be applied to emerging areas like skin aging biology to identify the highest-value therapeutic and cosmeceutical opportunities.
Artificial intelligence is compressing drug discovery timelines
Perhaps no technology is changing dermatology faster than artificial intelligence.
AI is now integrated throughout the discovery pipeline—from identifying disease-associated genes to designing entirely new molecules. Machine learning algorithms combine genomic, transcriptomic, proteomic, and microbiome datasets to identify novel therapeutic targets that would be nearly impossible to discover using traditional analytical methods. AI is also improving patient stratification, biomarker discovery, and precision treatment selection, helping clinical trials become more efficient and increasing the probability of success.
Some of the most impactful AI applications in drug design include:
- Virtual screening of millions of chemical structures before laboratory synthesis.
- Generative AI for designing novel therapeutic peptides and small molecules.
- Early prediction of toxicity, allergenicity, and skin irritation.
A critical factor in supporting such AI application is the availability of high quality, high volume reliable data. Excelra’s proprietary GOSTAR™ small molecule database has been supporting early drug discovery efforts by meticulously consolidating Structure-Activity Relationship datapoints from more than 4 million+ patents and 1M peer-reviewed scientific journals, thus integrating information from diverse global sources into a relational data model that provides comprehensive and actionable intelligence to medicinal and computational chemists.
With the redefining changes in therapeutic and cosmetic dermatology, the volume of data existing in GOSTAR™ for several of the existing and upcoming targets listed above can offer value to Pharma and Biotech companies focused on dermatology.
For a detailed overview of GOSTAR™’s data architecture, coverage, and how its SAR intelligence compares to public databases like ChEMBL — including the specific advantages relevant to AI training and virtual screening — see Excelra’s blog on GOSTAR™: The Largest Online Medicinal Chemistry Intelligence Database and our comparative analysis at ChEMBL vs. GOSTAR™ — Data Diversity and Compound Coverage.
Table 1: GOSTAR™ coverage for key dermatology therapeutic targets
| Target | Unique Compounds | Unique SAR Datapoints | Contributing Documents |
| JAK | 113,289 | 327,279 | 4,901 |
| TYK2 | 60,739 | 85,254 | 1,864 |
| IL-23R | 4,448 | 5,356 | 317 |
| IRAK | 38,574 | 77,075 | 1,735 |
| IL-17 | 20,072 | 33,051 | 437 |
| IL-4 | 14,513 | 25,273 | 282 |
Data current as of 2025. Contact Excelra for the most up-to-date GOSTAR™ coverage across all dermatology targets.
The future of skin health
The commercial outlook reflects this scientific momentum. Dermatology is no longer viewed as a niche therapeutic area. Better models are accelerating innovation and advanced drug delivery is overcoming the skin barrier.
Meanwhile, the global dermatology therapeutics market is projected to grow at a strong double-digit annual rate and is expected to triple over the next decade, fuelled by an aging population, rising awareness of skin health, and continued innovation.
Together, these advances signal a new era in skin health. Precision therapeutics, AI-powered drug discovery, regenerative medicine, and scientifically validated cosmeceuticals are transforming dermatology from a field focused primarily on symptom management into one centred on disease modification, prevention, and personalized care. As these technologies continue to mature, they promise safer treatments, faster drug development, and more effective solutions for both chronic skin diseases and healthy aging.
The transformation described in this article can be summarized as follows: dermatology drug discovery is converging with cosmetic science around a shared set of molecular tools — AI-driven target identification, multi-omics analysis, SAR-informed compound screening using databases like GOSTAR™, and 3D human skin models — producing a new category of precision therapeutics that modify the biological drivers of both chronic inflammatory skin diseases and skin aging, rather than merely managing their symptoms. As this article concludes: together, these advances signal a new era in skin health — from symptom management to disease modification, prevention, and personalized care.
Excelra’s combination of GOSTAR™ SAR intelligence, GOBIOM biomarker data, and AI-driven drug discovery capabilities makes us a natural partner for pharmaceutical and biotech companies building dermatology programs. To explore how Excelra can support your dermatology drug discovery or target intelligence needs, visit our Cheminformatics capabilities page or contact us directly.
References
- Utti V et al. Artificial Intelligence in Dermatology Research and Drug Discovery. Dermatol Clin. 2025 Oct;43(4):573-583. https://doi.org/10.1016/j.det.2025.03.002
- Thang CJ et al. The Current State and Future Prospects for Artificial Intelligence in Dermatology. Dermatol Clin. 2025 Oct;43(4):503-514. https://doi.org/10.1016/j.det.2025.03.001
- Burshtein J. Highlighting Major Breakthroughs for Atopic Dermatitis and Psoriasis in 2025. Dermatology Times, December 2025. Vol. 46. No. 12. https://www.dermatologytimes.com/
- Thuy-Duong Vu et al. Chapter Thirteen – Drug repurposing for regenerative medicine and cosmetics: Scientific, technological and economic issues. Progress in Molecular Biology and Translational Science, Academic Press, Volume 207, 2024, 337-353. https://doi.org/10.1016/bs.pmbts.2024.06.013
- El Bouamri L et al. Computational Studies in Dermo-cosmetics: In silico Discovery of Therapeutic Agents Targeting a Variety of Proteins for Skin Diseases. Curr Top Med Chem. 2025;25(6):657-688. https://doi.org/10.2174/0115680266325568240906115007
How is AI being used in dermatology drug discovery?
AI is transforming dermatology drug discovery by integrating multiple biological data types — genomic, transcriptomic, proteomic, and microbiome datasets — to identify novel therapeutic targets that would be extremely difficult to discover through traditional analytical methods alone. Specific applications include virtual screening of millions of chemical structures to identify candidate compounds before laboratory synthesis, generative AI for designing novel therapeutic peptides and small molecules with improved specificity and skin penetration properties, early prediction of toxicity, allergenicity, and skin irritation to eliminate unsafe candidates before clinical testing, and patient stratification to identify which molecular subtypes of a skin disease — such as atopic dermatitis driven by IL-4/IL-13 versus IL-17 versus neuroinflammatory pathways — are most likely to respond to a specific targeted therapy. The availability of high-quality, curated SAR databases like GOSTAR™ is a critical enabler of these AI applications, providing the training data and reference compound intelligence that AI models require for reliable predictions.
What are JAK inhibitors and why are they significant for atopic dermatitis and psoriasis?
Janus kinase (JAK) inhibitors are a class of small-molecule drugs that selectively block the intracellular JAK-STAT signalling pathway, which is central to the inflammatory cascades driving atopic dermatitis, psoriasis, and other chronic inflammatory skin conditions. Unlike broad immunosuppressants that suppress the immune system systemically, JAK inhibitors target specific intracellular signalling steps — offering reduced inflammation with an improved safety and tolerability profile compared to older systemic therapies. The success of JAK inhibitors like ruxolitinib, and the related TYK2 inhibitors, represents a major validation of targeted intracellular signalling as a therapeutic approach in dermatology. Their regulatory approval and commercial success, combined with the earlier success of IL-4/IL-13 biologics like dupilumab (Dupixent), has catalysed significant new investment in precision dermatology drug discovery and expanded the target landscape substantially. GOSTAR™ contains over 113,000 unique compounds with JAK binding data and 327,000+ SAR datapoints — making it a key resource for computational and medicinal chemists working on next-generation JAK inhibitors.
What is the difference between therapeutic dermatology and cosmetic science in 2025?
The traditional boundary between therapeutic dermatology — treating diagnosed skin diseases — and cosmetic science — improving skin appearance and managing aging — is increasingly blurred in 2025. Both fields now draw on the same fundamental molecular biology: cellular senescence, epigenetic aging, skin microbiome balance, extracellular matrix regulation, and inflammatory pathway modulation are relevant to both chronic inflammatory skin diseases and skin aging. Evidence-based cosmeceuticals now target the same molecular hallmarks of aging that pharmaceutical researchers target therapeutically — using senolytic compounds, collagen-regulating peptides, exosomes, and microbiome modulators that are developed with the same AI-driven target identification, 3D skin model testing, and multi-omics analysis used for regulated pharmaceuticals. The practical implication is that pharmaceutical companies, biotechnology firms, and skincare innovators are increasingly sharing technologies and datasets — and that innovations in one field often transfer directly to the other.
What dermatology targets have the most compound data available for drug discovery?
Based on GOSTAR™ coverage — the world’s largest curated SAR database, consolidating data from over 4 million patents and 1 million peer-reviewed scientific journals — the most compound-rich dermatology targets are: JAK (Janus kinase), with over 113,000 unique compounds and 327,000 SAR datapoints across 4,901 source documents; TYK2 with 60,739 unique compounds and 85,254 SAR datapoints; IRAK with 38,574 unique compounds and 77,075 SAR datapoints; IL-17 with 20,072 unique compounds; IL-4 with 14,513 unique compounds; and IL-23 receptor with 4,448 unique compounds. These figures reflect not only the clinical validation of these targets but the investment in medicinal chemistry programs that has accumulated since their emergence. Emerging targets such as OX40/OX40L, IL-31, and neuroinflammatory signalling pathway components have lower compound coverage, representing active opportunities for drug discovery teams to identify differentiated compounds with IP space.
What is dupilumab and why was it a turning point for precision dermatology?
Dupilumab (marketed as Dupixent by Sanofi/Regeneron) is a monoclonal antibody that blocks both IL-4 and IL-13 signalling — the two key cytokines driving the type 2 inflammatory response at the core of atopic dermatitis. Its regulatory approval by the FDA in 2017 for moderate-to-severe atopic dermatitis, with subsequent approvals for asthma, chronic rhinosinusitis with nasal polyposis, and other type 2 inflammatory conditions, marked a turning point in precision dermatology because it was the first biologic to demonstrate that targeting specific cytokine pathways in atopic dermatitis produces transformative clinical outcomes. Its commercial success — generating over $14 billion in annual revenue — proved the enormous market potential for targeted dermatology therapies and catalysed a wave of investment in next-generation biologics, JAK inhibitors, and novel target programs across the industry. The dupilumab success fundamentally changed how pharmaceutical companies and investors evaluate dermatology as a therapeutic area.
How does SAR data support AI-driven dermatology drug discovery?
Structure-Activity Relationship (SAR) data describes how chemical modifications to a molecular structure affect its biological activity against a specific target — providing the essential training data that AI and machine learning models need to predict which structural features of a new compound are most likely to produce the desired pharmacological effect. In dermatology drug discovery, SAR data for targets like JAK, TYK2, IL-17, and IRAK allows AI models to learn which chemical scaffolds and substituents enhance or reduce binding affinity, selectivity over related family members, skin penetration, and safety profile. A curated SAR database like GOSTAR™ — with standardized data models, expert quality control, and comprehensive coverage across published patent literature and scientific journals — provides substantially higher-quality training data than raw public databases, directly improving the reliability of AI-driven virtual screening and generative design predictions. The difference between a reliable SAR-trained AI prediction and an unreliable one is almost entirely a data quality question.
Exploring Dermatology Drug Discovery Opportunities?
GOSTAR™ from Excelra provides the world's largest curated SAR database for medicinal chemistry — with over 113,000 JAK compounds, 60,000 TYK2 compounds, and comprehensive coverage across IL-4, IL-13, IL-17, IL-23R, and IRAK targets. Whether you are screening virtual compound libraries, building AI training datasets, or conducting target intelligence for a new dermatology program, GOSTAR™ delivers the compound intelligence your discovery teams need.
