Drug Discovery용 인공지능(AI) 시장 예측(-2030년) : 구성요소별, 치료 분야별, 기술별, 용도별, 최종사용자별, 지역별 세계 분석

According to Stratistics MRC, the Global Artificial Intelligence in Drug Discovery Market is accounted for $1.4 billion in 2023 and is expected to reach $9.8 billion by 2030 growing at a CAGR of 31.6% during the forecast period. Artificial intelligence (AI) in the drug discovery market is the application of AI and machine learning techniques to streamline and enhance the drug development process. It utilizes algorithms to analyze vast datasets, predict potential drug candidates, optimize clinical trial designs, and identify novel drug targets. AI accelerates drug discovery by reducing costs, improving the efficiency of research, and increasing the likelihood of identifying successful drug candidates.

According to the International Diabetes Federation (IDF) report, in 2021, approximately 537 million adults (20-79 years) are living with diabetes across the globe. The total number of people living with diabetes is projected to rise to 643 million by 2030 and 783 million by 2045.

Market Dynamics:

Driver:

Rising prevalence of chronic and infectious diseases

AI technologies offer unparalleled capabilities to analyze complex biological data, accelerating drug development processes. With the increasing burden of diseases like cancer, diabetes, and antibiotic-resistant infections, AI aids in the rapid identification of potential drug candidates, target proteins, and treatment strategies. This not only expedites drug discovery but also improves the chances of success in clinical trials, reducing development costs. Furthermore, AI-driven approaches enable the repurposing of existing drugs and facilitate the discovery of novel therapies, ultimately addressing the urgent global healthcare need for more effective treatments.

Restraint:

Lack of data sets in the field of drug discovery

AI heavily relies on vast and diverse data sources for accurate analysis and prediction, but acquiring such data, especially in healthcare, is often challenging due to issues related to privacy, data sharing, and data standardization. Limited access to relevant and well-annotated datasets hinders the training and validation of AI models, potentially leading to suboptimal results and missed opportunities for drug discovery. Addressing these data limitations is crucial for unlocking AI's full potential in accelerating drug development and improving healthcare outcomes.

Opportunity:

Rising prevalence of chronic and infectious diseases

AI-driven solutions are well-suited to address the growing global health crisis by expediting the development of innovative therapeutics. With chronic diseases like cancer and diabetes reaching epidemic proportions and the emergence of antibiotic-resistant infections, AI's data-driven analytics can efficiently identify potential drug candidates, uncover novel targets, and streamline clinical trial designs. By harnessing the power of AI, researchers can accelerate drug discovery processes, optimize personalized treatment strategies, and ultimately, usher in a new era of more effective and accessible therapies to combat the rising burden of these diseases on a global scale.

Threat:

Limited understanding and expertise

The effective application of AI requires interdisciplinary knowledge spanning biology, chemistry, data science, and AI technologies. The shortage of experts who can bridge these domains can hinder the development and deployment of AI-driven solutions for drug discovery. Moreover, misconceptions about the capabilities and limitations of AI may lead to unrealistic expectations. Inadequate understanding can also result in poorly designed experiments or misinterpretation of AI-generated insights, potentially wasting resources and delaying drug development efforts. To harness the full potential of AI, addressing these knowledge gaps and fostering collaboration among experts is essential.

COVID-19 Impact:

The COVID-19 pandemic has had a profound impact on the artificial intelligence in drug discovery market. On one hand, it accelerated the adoption of AI-driven approaches, as researchers urgently sought solutions for drug repurposing and vaccine development. AI played a critical role in identifying potential drug candidates and optimizing clinical trial designs, significantly shortening development timelines. However, the pandemic also disrupted research efforts, delayed clinical trials, and redirected resources, causing setbacks in AI-based drug discovery projects. Moreover, the increased demand for AI expertise and data resources strained the field's capacity, highlighting the need for infrastructure improvements and data sharing initiatives.

The oncology segment is expected to be the largest during the forecast period

The oncology segment is expected to have lucrative growth. AI is revolutionizing oncology drug discovery by rapidly analyzing extensive genomic, proteomic, and clinical data. Machine learning algorithms identify unique genetic mutations, potential drug targets, and predict drug responses, facilitating the development of precision medicines tailored to individual cancer patients. Furthermore, AI enables the repurposing of existing drugs for novel oncology applications, reducing development costs and timelines. With the ever-growing cancer burden worldwide, AI-powered drug discovery offers unprecedented opportunities to uncover groundbreaking therapies, optimize treatment regimens, and improve patient outcomes in the challenging realm of oncology.

The preclinical testing segment is expected to have the highest CAGR during the forecast period

The preclinical testing segment is anticipated to witness the fastest CAGR growth during the forecast period. AI aids in the identification of potential drug candidates by analyzing vast datasets, predicting compound properties, and assessing their safety profiles. Through virtual screening and predictive modelling, AI accelerates the selection of lead compounds for further evaluation, reducing the time and cost associated with preclinical research. Additionally, AI-powered platforms assist in designing more targeted experiments, optimizing study protocols, and predicting potential toxicity issues early in drug development. This innovative approach enhances the efficiency and success rates of preclinical testing, ultimately expediting the delivery of safer and more effective drugs to market.

Region with largest share:

North America holds a significant share in the Artificial Intelligence in Drug Discovery Market, driven by its advanced healthcare infrastructure, strong research and development capabilities, and supportive regulatory environment. The region boasts a high adoption rate of IoT-enabled medical devices, including wearable health trackers and remote monitoring systems, as healthcare providers seek to improve patient care and outcomes. North America's investment in telemedicine and data-driven healthcare, along with its focus on patient-centric care models, positions it as a frontrunner in leveraging IoT technology to transform and enhance the delivery of healthcare services.

Region with highest CAGR:

Asia Pacific is projected to have the highest CAGR over the forecast period, fuelled by its expanding population, increasing healthcare needs, and growing adoption of digital technologies. With the support of government initiatives and a growing tech-savvy consumer base, Artificial Intelligence in Drug Discovery are rapidly gaining acceptance. In addition to improving patient care, they address challenges like remote patient monitoring in rural areas. Asia Pacific's vast market potential, coupled with its commitment to healthcare innovation, positions it as a significant player in the global Artificial Intelligence in Drug Discovery Market, fostering transformative advancements in healthcare delivery.

Key players in the market:

Some of the key players in Artificial Intelligence in Drug Discovery market include: Cyclica, Deep Genomics, Euretos, Alphabet, Atomwise, Benevolent AI, Berg Health, BioSymetrics, Exscientia, Insilico Medicine, GNS Healthcare, IBM, Insitro, Microsoft, Neumora, Notable, Nvidia Corporation, PathAI and Recursion.

Key Developments:

In November 2022, Exscientia collaborated with the University of Texas MD Anderson Cancer Center to use its patient-centric artificial intelligence technology for novel small molecule drug discovery and development using the expertise of MD Anderson. This strategy helped the company to expand and grow.

In August 2022, GNS Healthcare collaborated with Servier, a global pharmaceutical group to advance drug discovery, translational, and clinical development efforts in multiple myeloma (MM). This strategy helped the company to expand its service offering.

Components Covered:

• Software
• Services

Therapeutic Areas Covered:

• Oncology
• Neurodegenerative Diseases
• Inflammatory
• Infectious Diseases
• Metabolic Diseases
• Rare Diseases
• Cardiovascular Diseases
• Other Therapeutic Areas

Technologies Covered:

• Machine Learning
• Other Technologies

Applications Covered:

• Molecular Library Screening
• Target Identification
• Drug Optimization and Repurposing
• De novo Drug Designing
• Preclinical Testing

End Users Covered:

• Pharmaceutical and Biotechnology Companies
• Contract Research Organization (CROs)
• Academics & Research
• Other End Users

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Technology Analysis
• 3.7 Application Analysis
• 3.8 End User Analysis
• 3.9 Emerging Markets
• 3.10 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Artificial Intelligence in Drug Discovery Market, By Component

• 5.1 Introduction
• 5.2 Software
• 5.3 Services

6 Global Artificial Intelligence in Drug Discovery Market, By Therapeutic Area

• 6.1 Introduction
• 6.2 Oncology
• 6.3 Neurodegenerative Diseases
• 6.4 Inflammatory
• 6.5 Infectious Diseases
• 6.6 Metabolic Diseases
• 6.7 Rare Diseases
• 6.8 Cardiovascular Diseases
• 6.9 Other Therapeutic Areas

7 Global Artificial Intelligence in Drug Discovery Market, By Technology

• 7.1 Introduction
• 7.2 Machine Learning
  • 7.2.1 Deep Learning
  • 7.2.2 Supervised Learning
  • 7.2.3 Unsupervised Learning
  • 7.2.4 Other Machine Learning Technologies
• 7.3 Other Technologies

8 Global Artificial Intelligence in Drug Discovery Market, By Application

• 8.1 Introduction
• 8.2 Molecular Library Screening
• 8.3 Target Identification
• 8.4 Drug Optimization and Repurposing
• 8.5 De novo Drug Designing
• 8.6 Preclinical Testing

9 Global Artificial Intelligence in Drug Discovery Market, By End User

• 9.1 Introduction
• 9.2 Pharmaceutical and Biotechnology Companies
• 9.3 Contract Research Organization (CROs)
• 9.4 Academics & Research
• 9.5 Other End Users

10 Global Artificial Intelligence in Drug Discovery Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 Cyclica
• 12.2 Deep Genomics
• 12.3 Euretos
• 12.4 Alphabet
• 12.5 Atomwise
• 12.6 Benevolent AI
• 12.7 Berg Health
• 12.8 BioSymetrics
• 12.9 Exscientia
• 12.10 Insilico Medicine
• 12.11 GNS Healthcare
• 12.12 IBM
• 12.13 Insitro
• 12.14 Microsoft
• 12.15 Neumora
• 12.16 Notable
• 12.17 Nvidia Corporation
• 12.18 PathAI
• 12.19 Recursion