AI-Powered Drug Discovery in Dermatology Market Size is valued at USD 234.7 Mn in 2024 and is predicted to reach USD 1,924.6 Bn by the year 2034 at a 24.2% CAGR during the forecast period for 2025-2034.
AI-driven drug discovery in dermatology utilizes sophisticated machine learning and deep learning technology to expedite the creation of therapies for skin conditions including psoriasis, atopic dermatitis, acne, and skin malignancies. Through the analysis of intricate biological data, such as genomes, proteomics, and high-resolution dermatological pictures, AI might identify new therapeutic targets, forecast compound efficacy, and propose medication repurposing possibilities. AI-powered solutions greatly improve diagnostic care's precision, effectiveness, and results. The ability of AI to scan medical images to identify and categorize problems like psoriasis, skin cancer, and ulcers is a particularly interesting application of AI in dermatology. AI ensures accurate skin lesion recognition and differentiation by analyzing each image's pixels and comparing the results with a dermatologist's expertise. The increasing prevalence of skin disorders combined with AI's proven capacity to improve diagnosis accuracy and efficiency is propelling the use of AI-powered drug discovery in the dermatology market. During the forecast period, these advancements are expected to propel the expansion of AI-powered drug discovery in the dermatology market by facilitating better target identification, optimizing treatment strategies, and improving patient outcomes.
Additionally, the partnerships between pharmaceutical and technology businesses offer a substantial market expansion possibility for the discovery of AI-powered drug discovery in dermatology. These partnerships result in more efficient and innovative drug development processes by combining the industry knowledge and resources of pharmaceutical corporations with the innovative solutions and data-driven approach of technology businesses.
The AI-Powered Drug Discovery in Dermatology market is segmented based on AI Model, Drug Type, Indication, and End-user. Based on AI Model, the market is segmented into Machine Learning (ML) (Supervised Learning, Reinforcement Learning, Unsupervised Learning, Others), Deep Learning (Convolutional Neural Networks (CNNs), Neural Networks, Others), Natural Language Processing (NLP), Generative Models (Transformer Models, GANs (Generative Adversarial Networks), Others), and Others. By Drug Type, the market is segmented into Small Molecules, Biosimilars, Natural Compounds, Biologics, RNA-Based Therapeutics, and Others. By Indication, the market is segmented into Psoriasis, Skin Cancer, Vitiligo, Wound Healing, Atopic Dermatitis (Eczema), Acne, Rare Dermatological Disorders, and Others. By End-user, the market is segmented into Biotechnology Firms, Pharmaceutical Companies, Academic and Research Institutes, Healthcare Providers, and Contract Research Organizations (CROs).
The small molecules category is expected to hold a major global market share in 2024 because they can target particular receptors and signaling molecules, enabling more targeted and precise treatments. Small molecules are also more readily absorbed and distributed throughout the body, which makes them an effective choice for treatments involving the skin. The category for small-molecule drug discoveries in dermatology is further fueled by their great commercial potential and low development costs, which make them an appealing research area for both pharmaceutical companies and academic institutions.
In the global market for AI-powered drug discovery in dermatology, the pharmaceutical firms segment is anticipated to hold the biggest revenue share. Its established expertise in medication development, extensive resources, and access to large patient populations for clinical trials are the reasons behind this. Pharmaceutical corporations are making significant investments in AI technology in an effort to speed up the drug discovery process, increase treatment effectiveness, and lower development costs. These companies are also well-positioned to drive the adoption of AI-powered drug discovery in dermatology since they have a strong presence in the healthcare business and a clear understanding of regulatory requirements.
The North American AI-Powered Drug Discovery in Dermatology market is expected to register the highest market share in revenue in the near future, explained by a number of factors, including the region's advanced healthcare facilities, high levels of investment in biotechnology and research, and an emphasis on developing personalized medicine. Furthermore, a favorable environment for AI-powered drug discovery in dermatology has been created by the rising occuance of skin disorders and the increasing need for effective and efficient remedies. In addition, Asia Pacific is projected to grow rapidly in the global AI-Powered Drug Discovery in Dermatology market. The growing usage of AI AI-powered drug discovery in dermatology has been driven by the region's rising frequency of skin diseases as well as the demand for quick and accurate dermatological diagnoses. Due to its focus on technological advancements and commitment to enhancing dermatological treatment, Asia Pacific has dominated the market for AI-powered drug discovery in dermatology.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 234.7 Mn |
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Revenue Forecast In 2034 |
USD 1,924.6 Mn |
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Growth Rate CAGR |
CAGR of 24.2% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Mn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By AI Model, Drug Type, Indication, and End-user |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
Almirall, Quantificare, Google, Microsoft, SCARLETRED, QIMA Monasterium, CytoReason LTD, Atomwise Inc., and Others. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI-Powered Drug Discovery in Dermatology Market Snapshot
Chapter 4. Global AI-Powered Drug Discovery in Dermatology Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Porter's Five Forces Analysis
4.7. Incremental Opportunity Analysis (US$ MN), 2024-2034
4.8. Competitive Landscape & Market Share Analysis, By Key Player (2023)
4.9. Use/impact of AI on AI-Powered Drug Discovery in Dermatology Market Industry Trends
4.10. Global AI-Powered Drug Discovery in Dermatology Market Penetration & Growth Prospect Mapping (US$ Mn), 2021-2034
Chapter 5. AI-Powered Drug Discovery in Dermatology Market Segmentation 1: By Drug Type, Estimates & Trend Analysis
5.1. Market Share by Drug Type, 2024 & 2034
5.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following Drug Type:
5.2.1. Small Molecules
5.2.2. Biologics
5.2.3. Biosimilars
5.2.4. Natural Compounds
5.2.5. RNA-Based Therapeutics
5.2.6. Others
Chapter 6. AI-Powered Drug Discovery in Dermatology Market Segmentation 2: By AI Model, Estimates & Trend Analysis
6.1. Market Share by AI Model, 2024 & 2034
6.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following AI Model:
6.2.1. Machine Learning (ML)
6.2.1.1. Supervised Learning
6.2.1.2. Unsupervised Learning
6.2.1.3. Reinforcement Learning
6.2.1.4. Others
6.2.2. Deep Learning
6.2.2.1. Neural Networks
6.2.2.2. Convolutional Neural Networks (CNNs)
6.2.2.3. Others
6.2.3. Natural Language Processing (NLP)
6.2.4. Generative Models
6.2.4.1. GANs (Generative Adversarial Networks)
6.2.4.2. Transformer Models
6.2.4.3. Others
Chapter 7. AI-Powered Drug Discovery in Dermatology Market Segmentation 3: By Indication, Estimates & Trend Analysis
7.1. Market Share by Indication, 2024 & 2034
7.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following Indication:
7.2.1. Psoriasis
7.2.2. Atopic Dermatitis (Eczema)
7.2.3. Acne
7.2.4. Skin Cancer (e.g., Melanoma, Basal Cell Carcinoma)
7.2.5. Vitiligo
7.2.6. Wound Healing
7.2.7. Rare Dermatological Disorders
7.2.8. Others
Chapter 8. AI-Powered Drug Discovery in Dermatology Market Segmentation 3: By End User, Estimates & Trend Analysis
8.1. Market Share by End User, 2024 & 2034
8.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following End User:
8.2.1. Pharmaceutical Companies
8.2.2. Biotechnology Firms
8.2.3. Academic and Research Institutes
8.2.4. Contract Research Organizations (CROs)
8.2.5. Healthcare Providers
Chapter 9. AI-Powered Drug Discovery in Dermatology Market Segmentation 5: Regional Estimates & Trend Analysis
9.1. Global AI-Powered Drug Discovery in Dermatology Market, Regional Snapshot 2024 & 2034
9.2. North America
9.2.1. North America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
9.2.1.1. US
9.2.1.2. Canada
9.2.2. North America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Drug Type, 2021-2034
9.2.3. North America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by AI Model, 2021-2034
9.2.4. North America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Indication, 2021-2034
9.2.5. North America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.3. Europe
9.3.1. Europe AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
9.3.1.1. Germany
9.3.1.2. U.K.
9.3.1.3. France
9.3.1.4. Italy
9.3.1.5. Spain
9.3.1.6. Rest of Europe
9.3.2. Europe AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Drug Type, 2021-2034
9.3.3. Europe AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by AI Model, 2021-2034
9.3.4. Europe AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Indication, 2021-2034
9.3.5. Europe AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.4. Asia Pacific
9.4.1. Asia Pacific AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
9.4.1.1. India
9.4.1.2. China
9.4.1.3. Japan
9.4.1.4. Australia
9.4.1.5. South Korea
9.4.1.6. Hong Kong
9.4.1.7. Southeast Asia
9.4.1.8. Rest of Asia Pacific
9.4.2. Asia Pacific AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Drug Type, 2021-2034
9.4.3. Asia Pacific AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by AI Model, 2021-2034
9.4.4. Asia Pacific AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Indication, 2021-2034
9.4.5. Asia Pacific AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.5. Latin America
9.5.1. Latin America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
9.5.1.1. Brazil
9.5.1.2. Mexico
9.5.1.3. Rest of Latin America
9.5.2. Latin America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Drug Type, 2021-2034
9.5.3. Latin America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by AI Model, 2021-2034
9.5.4. Latin America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Indication, 2021-2034
9.5.5. Latin America AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.6. Middle East & Africa
9.6.1. Middle East & Africa Wind Turbine Rotor Blade Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.6.1.1. GCC Countries
9.6.1.2. Israel
9.6.1.3. South Africa
9.6.1.4. Rest of Middle East and Africa
9.6.2. Middle East & Africa AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Drug Type, 2021-2034
9.6.3. Middle East & Africa AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by AI Model, 2021-2034
9.6.4. Middle East & Africa AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by Indication, 2021-2034
9.6.5. Middle East & Africa AI-Powered Drug Discovery in Dermatology Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
Chapter 10. Competitive Landscape
10.1. Major Mergers and Acquisitions/Strategic Alliances
10.2. Company Profiles
10.2.1. Almirall
10.2.1.1. Business Overview
10.2.1.2. Key Drug Type/Service Overview
10.2.1.3. Financial Performance
10.2.1.4. Geographical Presence
10.2.1.5. Recent Developments with Business Strategy
10.2.2. SCARLETRED
10.2.3. QIMA Monasterium
10.2.4. Quantificare
10.2.5. Google
10.2.6. Microsoft
10.2.7. CytoReason LTD
10.2.8. Atomwise Inc.
10.2.9. Among Others
AI-Powered Drug Discovery in Dermatology Market By AI Model-
AI-Powered Drug Discovery in Dermatology Market By Drug Type-
AI-Powered Drug Discovery in Dermatology Market By Indication-
AI-Powered Drug Discovery in Dermatology Market By End-User-
AI-Powered Drug Discovery in Dermatology Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
To know more about the research methodology used for this study, kindly contact us/click here.
