HEALTHCARE
Global Artificial Intelligence in Drug Discovery Market - Industry Trends and Forecast to 2032
REPORT OVERVIEW
Global Artificial Intelligence in Drug Discovery Market, By Offering (Software and Services), Technology (Machine Learning, Natural Language Processing, Context-aware Processing, and Other Technologies), Therapeutic Area (Oncology, Infectious Diseases, Neurology, Metabolic Diseases, Cardiovascular Diseases, Immunology, and Other Therapeutic Areas), Process (Target Identification & Selection, Target Validation, Hit Identification & Prioritization, Hit-to-lead Identification/ Lead generation, Lead Optimization, and Candidate Selection & Validation), Drug Type (Small Molecule and Large Molecule), Use Cases (Understanding Disease, Small Molecule Design and Optimization, Vaccine Design and Optimization, Antibody & Other Biologics Design and Optimization, Safety and Toxicity), End User (Pharmaceutical & Biotechnology Companies, Contract Research Organizations, Research Centers and Academic & Government Institutes), Region (North America, Europe, Asia-Pacific, South America, Middle East and Africa) – Industry Trends and Forecast to 2032.
Market Insights
The global artificial intelligence in drug discovery market size is valued to be USD xx million in 2023 and is expected to reach USD xx million by 2032, and it is expected to register a CAGR of xx% over the forecast period 2024-2032.
The use of advanced computational techniques, machine learning algorithms, and data analytics to accelerate and improve the process of discovering new therapeutic compounds, identifying potential drug candidates, and optimizing the drug development pipeline is referred to as artificial intelligence (AI) in drug discovery. AI is critical in the drug discovery and development process, analysing massive volumes of biological, chemical, and clinical data to detect patterns, anticipate molecular interactions, and assist decision-making.
The incorporation of AI in drug discovery seeks to accelerate the identification of innovative drug candidates, minimize development costs, and improve overall pipeline efficiency. It has the potential to transform the pharmaceutical business by bringing novel treatments to patients more quickly and addressing unmet medical needs.
Market Dynamics
DRIVERS
- Rising prevalence of chronic diseases
- Rising technological advancements
RESTRAINTS
- Strict regulations and guidelines
- High cost associated with technology
OPPORTUNITIES
- Increasing investments in research and development activities
- Increasing strategic initiatives
CHALLENGES
- Shortage of skilled professional
- Ambiguous regulatory guidelines for medical software
SEGMENTATION
MARKET SEGMENTATION
- Offering
- Software
- Services
- Technology
- Machine Learning
- Deep Learning
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
- Other Machine Learning Technologies
- Natural Language Processing
- Context-aware Processing
- Other Technologies
- Therapeutic Area
- Oncology
- Infectious Diseases
- Neurology
- Metabolic Diseases
- Cardiovascular Diseases
- Immunology
- Other Therapeutic Areas
- Process
- Target Identification & Selection
- Target Validation
- Hit Identification & Prioritization
- Hit-to-lead Identification/ Lead generation
- Lead Optimization
- Candidate Selection & Validation
- Drug Type
- Small Molecule
- Large Molecule
- Use Cases
- Understanding Disease
- Small Molecule Design and Optimization
- Vaccine Design and Optimization
- Antibody & Other Biologics Design and Optimization
- Safety and Toxicity
- End User
- Pharmaceutical & Biotechnology Companies
- Contract Research Organizations
- Research Centers and Academic & Government Institutes
The respective global report is completely customizable specific to regions (North America, Europe, Asia-Pacific, South America, Middle East and Africa), countries, and segments as per the client requirements.
REGIONAL SEGMENTATION
- North America
- U.S.
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- U.K.
- France
- Italy
- Spain
- Russia
- The Netherlands
- Belgium
- Turkey
- Rest of Europe
- Asia-Pacific
- China
- India
- Japan
- South Korea
- Singapore
- Malaysia
- Australia
- Thailand
- Philippines
- Rest of Asia-Pacific
- South America
- Brazil
- Argentina
- Chile
- Colombia
- Rest of South America
- Middle East and Africa
- Kingdom of Saudi Arabia
- South Africa
- U.A.E.
- Egypt
- Rest of Middle East and Africa
KEY MARKET PLAYERS
- Cardinal Health
- Advanced Accelerator Applications
- A Novartis Company
- Lantheus
- Curium
- GE HealthCare
- Jubilant Radiopharma
- China lsotope & Radiation Corporation.
- Siemens Healthcare GmbH
- Bracco
Table OF CONTENTS
- SECTION 1 - INTRODUCTION
- 1.1 Taxonomy
- 1.2 Market Overview
- 1.3 Currency and Limitations
- 1.3.1 Currency
- 1.3.2 Limitations
- 1.4 Key Competitors
- SECTION 2 - RESEARCH METHODOLOGY
- 2.1 Research Approach
- 2.2 Data Collection and Validation
- 2.2.1 Secondary Research
- 2.2.2 Primary Research
- 2.3 Market Assessment
- 2.3.1 Market Size Estimation
- 2.3.2 Bottom-up Approach
- 2.3.3 Top-down Approach
- 2.3.4 Growth Forecast
- 2.4 Market Study Assumptions
- 2.5 Data Sources
- SECTION 3 - EXECUTIVE SUMMARY
- 3.1 Global Artificial Intelligence in Drug Discovery Market, by Offering
- 3.2 Global Artificial Intelligence in Drug Discovery Market, by Technology
- 3.3 Global Artificial Intelligence in Drug Discovery Market, by Therapeutic Area
- 3.4 Global Artificial Intelligence in Drug Discovery Market, by Process
- 3.5 Global Artificial Intelligence in Drug Discovery Market, by Drug Type
- 3.6 Global Artificial Intelligence in Drug Discovery Market, by Use Cases
- 3.7 Global Artificial Intelligence in Drug Discovery Market, by End User
- 3.8 Global Artificial Intelligence in Drug Discovery Market, by Geography
- 3.9 Market Position Grid
- SECTION 4 - PREMIUM INSIGHTS
- 4.1 Regulatory Framework
- 4.1.1 Standards
- 4.1.2 Regulatory Landscape
- 4.2 Value Chain Analysis
- 4.3 Supply Chain Analysis
- 4.4 COVID-19 Impact
- 4.5 Russia-Ukraine War Impact
- 4.6 PORTER's Five Force Analysis
- 4.7 PESTLE Analysis
- 4.8 SWOT Analysis
- 4.9 Go to Market Strategy
- 4.10 Opportunity Orbit
- 4.11 Multivariate Modelling
- 4.12 Pricing Analysis
- SECTION 5 - MARKET DYNAMICS
- 5.1 Trends
- 5.1.1 Trend 1
- 5.1.2 Trend 2
- 5.1.3 Trend 3
- 5.2 Drivers
- 5.2.1 Rising prevalence of chronic diseases
- 5.2.2 Rising technological advancements
- 5.2.3 Driver 3
- 5.2.4 Driver 4
- 5.3 Restraints
- 5.3.1 Strict regulations and guidelines
- 5.3.2 High cost associated with technology
- 5.3.3 Restraint 3
- 5.4 Opportunities
- 5.4.1 Increasing investments in research and development activities
- 5.4.2 Increasing strategic initiatives
- 5.4.3 Opportunity 3
- 5.4.4 Opportunity 4
- 5.5 Challenges
- 5.5.1 Shortage of skilled professional
- 5.5.2 Ambiguous regulatory guidelines for medical software
- 5.5.3 Challenge 3
- SECTION 6 - GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY OFFERING
- 6.1 Offering Summary
- 6.2 Market Attractive Index
- 6.3 Global Artificial Intelligence in Drug Discovery Market, by Offering (2019-2032)
- SECTION 7 - GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY TECHNOLOGY
- 7.1 Technology Summary
- 7.2 Market Attractive Index
- 7.3 Global Artificial Intelligence in Drug Discovery Market, by Technology (2019-2032)
- SECTION 8 - GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY THERAPEUTIC AREA
- 8.1 Therapeutic Area Summary
- 8.2 Market Attractive Index
- 8.3 Global Artificial Intelligence in Drug Discovery Market, by Therapeutic Area (2019-2032)
- SECTION 9 - GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY PROCESS
- 9.1 Process Summary
- 9.2 Market Attractive Index
- 9.3 Global Artificial Intelligence in Drug Discovery Market, by Process (2019-2032)
- SECTION 10 - GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY DRUG TYPE
- 10.1 Drug Type Summary
- 10.2 Market Attractive Index
- 10.3 Global Artificial Intelligence in Drug Discovery Market, by Drug Type (2019-2032)
- SECTION 11 - GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY USE CASES
- 11.1 Use Cases Summary
- 11.2 Market Attractive Index
- 11.3 Global Artificial Intelligence in Drug Discovery Market, by Use Cases (2019-2032)
- SECTION 12 - GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY END USER
- 12.1 End User Summary
- 12.2 Market Attractive Index
- 12.3 Global Artificial Intelligence in Drug Discovery Market, by End User (2019-2032)
- SECTION 13 - GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, BY GEOGRAPHY
- 13.1 Regional Summary
- 13.2 Market Attractive Index
- 13.3 Global Artificial Intelligence in Drug Discovery Market, by Geography (2019-2032)
- SECTION 14 - NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
- 14.1 North America Summary
- 14.2 Market Attractive Index
- 14.3 North America Artificial Intelligence in Drug Discovery Market, by Offering (2019-2032)
- 14.4 North America Artificial Intelligence in Drug Discovery Market, by Technology (2019-2032)
- 14.5 North America Artificial Intelligence in Drug Discovery Market, by Therapeutic Area (2019-2032)
- 14.6 North America Artificial Intelligence in Drug Discovery Market, by Process (2019-2032)
- 14.7 North America Artificial Intelligence in Drug Discovery Market, by Drug Type (2019-2032)
- 14.8 North America Artificial Intelligence in Drug Discovery Market, by Use Cases (2019-2032)
- 14.9 North America Artificial Intelligence in Drug Discovery Market, by End User (2019-2032)
- 14.10 North America Artificial Intelligence in Drug Discovery Market, by Country (2019-2032)
- 14.10.1 U.S.
- 14.10.2 Canada
- 14.10.3 Mexico
- 14.10.4 Rest of North America
- SECTION 15 - EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
- 15.1 Europe Summary
- 15.2 Market Attractive Index
- 15.3 Europe Artificial Intelligence in Drug Discovery Market, by Offering (2019-2032)
- 15.4 Europe Artificial Intelligence in Drug Discovery Market, by Technology (2019-2032)
- 15.5 Europe Artificial Intelligence in Drug Discovery Market, by Therapeutic Area (2019-2032)
- 15.6 Europe Artificial Intelligence in Drug Discovery Market, by Process (2019-2032)
- 15.7 Europe Artificial Intelligence in Drug Discovery Market, by Drug Type (2019-2032)
- 15.8 Europe Artificial Intelligence in Drug Discovery Market, by Use Cases (2019-2032)
- 15.9 Europe Artificial Intelligence in Drug Discovery Market, by End User (2019-2032)
- 15.10 Europe Artificial Intelligence in Drug Discovery Market, by Country (2019-2032)
- 15.10.1 Germany
- 15.10.2 U.K.
- 15.10.3 France
- 15.10.4 Italy
- 15.10.5 Spain
- 15.10.6 Russia
- 15.10.7 The Netherlands
- 15.10.8 Belgium
- 15.10.9 Turkey
- 15.10.10 Rest of Europe
- SECTION 16 - ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
- 16.1 Asia-Pacific Summary
- 16.2 Market Attractive Index
- 16.3 Asia-Pacific Artificial Intelligence in Drug Discovery Market, by Offering (2019-2032)
- 16.4 Asia-Pacific Artificial Intelligence in Drug Discovery Market, by Technology (2019-2032)
- 16.5 Asia-Pacific Artificial Intelligence in Drug Discovery Market, by Therapeutic Area (2019-2032)
- 16.6 Asia-Pacific Artificial Intelligence in Drug Discovery Market, by Process (2019-2032)
- 16.7 Asia-Pacific Artificial Intelligence in Drug Discovery Market, by Drug Type (2019-2032)
- 16.8 Asia-Pacific Artificial Intelligence in Drug Discovery Market, by Use Cases (2019-2032)
- 16.9 Asia-Pacific Artificial Intelligence in Drug Discovery Market, by End User (2019-2032)
- 16.10 Asia-Pacific Artificial Intelligence in Drug Discovery Market, by Country (2019-2032)
- 16.10.1 China
- 16.10.2 India
- 16.10.3 Japan
- 16.10.4 South Korea
- 16.10.5 Singapore
- 16.10.6 Malaysia
- 16.10.7 Australia
- 16.10.8 Thailand
- 16.10.9 Philippines
- 16.10.10 Rest of Asia-Pacific
- SECTION 17 - SOUTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
- 17.1 South America Summary
- 17.2 Market Attractive Index
- 17.3 South America Artificial Intelligence in Drug Discovery Market, by Offering (2019-2032)
- 17.4 South America Artificial Intelligence in Drug Discovery Market, by Technology (2019-2032)
- 17.5 South America Artificial Intelligence in Drug Discovery Market, by Therapeutic Area (2019-2032)
- 17.6 South America Artificial Intelligence in Drug Discovery Market, by Process (2019-2032)
- 17.7 South America Artificial Intelligence in Drug Discovery Market, by Drug Type (2019-2032)
- 17.8 South America Artificial Intelligence in Drug Discovery Market, by Use Cases (2019-2032)
- 17.9 South America Artificial Intelligence in Drug Discovery Market, by End User (2019-2032)
- 17.10 South America Artificial Intelligence in Drug Discovery Market, by Country (2019-2032)
- 17.10.1 Brazil
- 17.10.2 Argentina
- 17.10.3 Chile
- 17.10.4 Colombia
- 17.10.5 Rest of South America
- SECTION 18 - MIDDLE EAST AND AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET
- 18.1 Middle East and Africa Summary
- 18.2 Market Attractive Index
- 18.3 Middle East and Africa Artificial Intelligence in Drug Discovery Market, by Offering (2019-2032)
- 18.4 Middle East and Africa Artificial Intelligence in Drug Discovery Market, by Technology (2019-2032)
- 18.5 Middle East and Africa Artificial Intelligence in Drug Discovery Market, by Therapeutic Area (2019-2032)
- 18.6 Middle East and Africa Artificial Intelligence in Drug Discovery Market, by Process (2019-2032)
- 18.7 Middle East and Africa Artificial Intelligence in Drug Discovery Market, by Drug Type (2019-2032)
- 18.8 Middle East and Africa Artificial Intelligence in Drug Discovery Market, by Use Cases (2019-2032)
- 18.9 Middle East and Africa Artificial Intelligence in Drug Discovery Market, by End User (2019-2032)
- 18.10 Middle East and Africa Artificial Intelligence in Drug Discovery Market, by Country (2019-2032)
- 18.10.1 Kingdom of Saudi Arabia
- 18.10.2 South Africa
- 18.10.3 U.A.E.
- 18.10.4 Egypt
- 18.10.5 Rest of Middle East and Africa
- SECTION 19 - COMPANY SHARE ANALYSIS
- 19.1 Global Artificial Intelligence in Drug Discovery Market, Company Share Analysis
- 19.2 North America Artificial Intelligence in Drug Discovery Market, Company Share Analysis
- 19.3 Europe Artificial Intelligence in Drug Discovery Market, Company Share Analysis
- 19.4 Asia-Pacific Artificial Intelligence in Drug Discovery Market, Company Share Analysis
- SECTION 20 - COMPANY PROFILES
- 20.1 NVIDIA Corporation
- 20.1.1 Company Snapshot
- 20.1.2 Financial Overview
- 20.1.3 Product Portfolio
- 20.1.4 Recent Developments
- 20.2 IBM Corp.
- 20.2.1 Company Snapshot
- 20.2.2 Financial Overview
- 20.2.3 Product Portfolio
- 20.2.4 Recent Developments
- 20.3 Atomwise Inc.
- 20.3.1 Company Snapshot
- 20.3.2 Financial Overview
- 20.3.3 Product Portfolio
- 20.3.4 Recent Developments
- 20.4 Microsoft
- 20.4.1 Company Snapshot
- 20.4.2 Financial Overview
- 20.4.3 Product Portfolio
- 20.4.4 Recent Developments
- 20.5 Benevolent AI
- 20.5.1 Company Snapshot
- 20.5.2 Financial Overview
- 20.5.3 Product Portfolio
- 20.5.4 Recent Developments
- 20.6 Aria Pharmaceuticals, Inc.
- 20.6.1 Company Snapshot
- 20.6.2 Financial Overview
- 20.6.3 Product Portfolio
- 20.6.4 Recent Developments
- 20.7 DEEP GENOMICS
- 20.7.1 Company Snapshot
- 20.7.2 Financial Overview
- 20.7.3 Product Portfolio
- 20.7.4 Recent Developments
- 20.8 Exscientia
- 20.8.1 Company Snapshot
- 20.8.2 Financial Overview
- 20.8.3 Product Portfolio
- 20.8.4 Recent Developments
- 20.9 Cloud
- 20.9.1 Company Snapshot
- 20.9.2 Financial Overview
- 20.9.3 Product Portfolio
- 20.9.4 Recent Developments
- 20.10 Insilico Medicine
- 20.10.1 Company Snapshot
- 20.10.2 Financial Overview
- 20.10.3 Product Portfolio
- 20.10.4 Recent Developments
- SECTION 21 - RELATED REPORTS
- SECTION 22 - DISCLAIMER
RESEARCH METHODOLOGY
The research methodology employed in Uniprism Market Research involves four basic steps namely research and data collection, data pre-processing, modeling and forecasting, quality assurance and output.
RESEARCH AND DATA COLLECTION
A tripod model research technique is followed for research and data collection in which various approaches such as primary research, secondary research, and product mapping are considered.
Primary research basically involves the process of conducting personalized interviews with market related professionals of major market players, investors, distributors, vendors and many more.
The secondary research include data published by government, annual reports, press releases, investor presentations of companies, white papers, certified publications, annual manufacturing limit of the respective industries related to the market, production consumption analysis of certain products respective to the market and many more.
Below mention are few of the sources which we have considered while estimating the market size:
For instance,
- Research articles published on Technium
- Science and MDPI
- Research publications by government approved associations and societies
Product mapping means the process of mapping the list of products that a key player contributes to the market as well as estimating the revenue of those products in order to define the Global Company share analysis of the respective Global Company in global, regional, and country level markets.
DATA PRE-PROCESSING
The term "data pre-processing" refers to the collection of procedures and methods used to clean, modify, and make ready for analysis the raw data gathered during research and data collection. The completion of this phase is necessary to guarantee that the data are reliable, consistent, and appropriate for statistical analysis and other data-driven tasks. The data pre-processing ensures that the information gathered from research and data collection is comparable and expressed in standard units, by the integration of missing data pointers and algorithmic approaches.
MODELING AND FORECASTING
The process of developing mathematical, statistical, or computational representations of real-world occurrences or relationships is known as modelling. These models are intended to replicate and explain market interactions, interdependence, and dynamics. These models are used by Uniprism Market Research to acquire a better knowledge of numerous market characteristics such as customer preferences, pricing elasticity, competition dynamics, and more. Depending on the individual study aims, many types of models are utilized, such as regression models, econometric models, decision tree models, and machine learning models.
Forecasting is the process of predicting future market conditions, trends, and occurrences using past data and models. Forecasting is used by Uniprism Market Research to estimate future sales, demand for products or services, market growth, and other important performance metrics. Forecasting accurately can assist organizations in making educated decisions about resource allocation, pricing, inventory management, and marketing tactics.
We create standardized bottom-up or top-down models that scale by leveraging data science and machine learning technology. All our market models consider the unique market characteristics of each country. Forecasting is based on major market indicators and a combination of traditional methodologies, such as exponential smoothing, time series analysis, regression analysis, and more modern techniques such as machine learning algorithms are all forecasting methodologies. The method chosen is determined on the nature of the data and the specific forecasting aims.
QUALITY ASSURANCE AND OUTPUT
Quality assurance and output involves the process of validation, adjustments, further publications of key market indicators. Extensive plausibility and consistency tests are performed on derived time series to ensure the high degree of quality of our market analysis. This quality assurance procedure also includes rigorous inspection, validation, and editing by an experienced management team to assure the dependability of the published data.