Drug Discovery용 AI : 시장 인사이트, 경쟁 구도 시장 예측(2030년)

Artificial Intelligence (AI) in Drug Discovery Market by Type (De Novo Drugs Design and Optimization, Preclinical Testing, and Others), Application (Oncology, Cardiovascular, Infection Disease, and Others), End-User (Pharmaceutical & Biotechnology Companies, Contract Research Organizations, and Others), and Geography (North America, Europe, Asia-Pacific, and Rest of the World) is estimated to grow at a healthy CAGR forecast till 2030 owing to rising prevalence of diseases and growing interest in leveraging artificial intelligence in drug development

The Artificial Intelligence (AI) in drug discovery market is estimated to advance at a CAGR of 37.67% during the forecast period from 2024 to 2030. The AI in drug discovery market is witnessing positive growth owing to factors such as the rising prevalence of various diseases across the globe, advantages of AI in the pharmaceutical sector, and investments in drug research and development, among others. In addition, extensive partnerships and collaborations between public and private entities at the both national and international levels are further expected to boost the AI in drug discovery market during the forecast period from 2024 to 2030.

AI in Drug Discovery Market Dynamics:

One of the key aspects influencing AI growth in the drug discovery market is a high capital investment in the drug discovery and development process. Following the conventional method of drug discovery and drug development results in the consumption of 12-14 years till a final product, the authorized drug reaches the market for end-use. For example, as per the Pharmaceutical Research and Manufacturers of America, on average, a drug takes about 10 years to get to the market with clinical trials accounting for 6-7 years out of the total period. The same source further stated that the average cost to develop each successful drug comes out to be approximately USD 2.5 billion. The drug discovery step in the drug development process is extremely daunting as there is a vast chemical space, comprising more than 1060 molecules that may serve as the starting point for developing potential drugs. The advantages offered by AI in terms of machine learning, neural networks can recognize hit and lead compounds and provide a quicker validation of the drug target and optimization of the drug structure design. Therefore, all the aforementioned factors point towards the advantages of including AI in drug discovery process helping in faster identification of targets, lead compounds, and subsequent development of drugs, thereby presenting a positive growth outlook for the AI in drug discovery market during the forecast period from 2024 to 2030.

Another aspect of including AI in drug discovery and drug development process is leveraging the technology to understand the patterns in the already published data to identify trending areas of research for different diseases that may provide insights regarding any scientific progress that may be utilized in initiating a new drug development program. This is done by using natural language processing (NLP) that helps in data mining and creating interconnected knowledge graphs. These knowledge graphs are essentially a threading together of the data from different areas of drug development such as disease-related data, drug related data, or chemical/biological entity related data.

Certain companies are involved in finding new uses for already approved drugs. For instance, an AI-focused startup Healx makes use of such knowledge graphs to gain insights into rare diseases. The company is working on the data for 4,000 FDA-registered drugs and this approach has also yielded drug candidates for the company that showed efficacy in animal models. Thus, the application of artificial intelligence in sifting through humongous data to create knowledge graphs and identify potential lead compounds and disease areas is another aspect driving the growth of the AI in drug discovery market in coming years.

Furthermore, the adoption of AI solutions in the clinical trial process eliminates possible obstacles, helps in the reduction of clinical trial cycle time, and significantly improves the productivity and accuracy of the clinical trial process. Therefore, the adoption of these advanced AI solutions in drug discovery processes is gaining popularity amongst life science industry stakeholders.

However, knowledge gaps between biologists, chemists, and AI scientists as well as limitations of traditional machine learning tools in handling the volume of data generated in the pharmaceutical field may prove to be challenging factors for AI in drug discovery market growth.

AI in Drug Discovery Market Segment Analysis:

AI in Drug Discovery Market by Type (De Novo Drug Design and Optimization, Preclinical Testing, and Others), Application (Oncology, Cardiovascular, Infection Diseases, and Others), End-User (Pharmaceutical & Biotechnology Companies, Contract Research Organizations, and Others), and Geography (North America, Europe, Asia-Pacific, and Rest of the World)

In the type segment of the AI in drug discovery market, the de novo drug design category is estimated to account for a prominent revenue share in the AI drug discovery market in 2023. The advantages of artificial intelligence (AI) in de novo drug design and optimization are becoming increasingly evident. AI-driven de novo drug design leverages vast datasets and advanced computational techniques to generate new molecular targets without relying on prior information. This innovative approach significantly accelerates the drug discovery process and enhances efficiency.

One of the cutting-edge AI methodologies employed in de novo drug design is deep reinforcement learning (DRL). DRL integrates artificial neural networks with reinforcement learning architectures, enabling the system to learn and adapt through trial and error. A notable example of DRL in de novo drug design is the use of recurrent neural networks (RNNs). RNNs are particularly well-suited for analyzing sequential data, such as text or molecules represented as a sequence of characters like SMILES (Simplified Molecular Input Line Entry System).

RNNs operate by processing input data sequentially, one step at a time, allowing them to recognize and learn patterns within SMILES strings. This capability is crucial for generating chemically plausible molecules in the de novo design process. The molecules produced through this method are driven by chemical principles, ensuring their potential viability as drug candidates. The success of RNNs in de novo drug design underscores the transformative potential of AI in this field.

Considering these advantages, AI-driven de novo drug design is poised to become a key application area in drug discovery in the coming years. The ability of AI to expedite the drug development process and generate innovative molecular targets is expected to contribute significantly to market growth. As the pharmaceutical industry continues to embrace AI technologies, the impact on drug discovery and development will likely be profound, driving advancements and opening new avenues for therapeutic innovation during the forecast period from 2024 to 2030.

North America is expected to dominate the overall AI in drug discovery market:

Among all the regions, North America is estimated to amass the largest revenue share in the AI in drug discovery market in the year 2023. This can be ascribed to the presence of a large patient pool associated with various diseases including cancers, and neurological disorders which in turn drive the demand for various drugs with minimal side effects. Moreover, the extensive focus on clinical research and the presence of key players in the region from both the pharmaceutical as well as technology domains further help in the growth of North America AI in drug discovery market.

One of the key supporting factors for the growth of North America region in the AI in drug discovery market is the increasing prevalence of various diseases across the region. For example, one of the prominent reasons for the requirement for high number of drugs is the surge in the prevalence of cancers in the US. National Cancer Institute 2024 estimated that 2 million new cases of cancer will be diagnosed in the US by the end of 2024. Furthermore, it is estimated that prostate, lung, and colorectal cancers are to represent approximately 48% of all cancer diagnoses in men. For women, the most prevalent cancers are breast, lung, and colorectal, which are expected to account for about 51% of all new cancer diagnoses.

Additionally, the American Cancer Society reported that in 2022, approximately 287,850 new cases of invasive breast cancer were diagnosed in women in the US. Therefore, the increasing incidence of cancers such as breast cancer along with other cancer types in the country is expected to further drive the demand for AI in drug discovery. To leverage the same, the National Cancer Institute (NCI), based in the United States, The Cancer Moonshoot in partnership with the Department of Energy (DOE) supported two major partnerships to leverage their supercomputing abilities to support cancer research by identifying and interpret features of target molecules that support cancer development; the second initiative being the RAS initiative to study the interaction of KRAS protein with the cell membrane using computational methods.

Therefore, the rising prevalence of cancers in the United States is boosting the development of cancer drug development, thereby providing a conducive environment for the AI in drug discovery market to grow in the United States.

Similar to the United States, Canada also has a robust ecosystem for AI in drug discovery process which can be supported by the fact that numerous startups are working in the country amalgamating both AI and drug development. For instance, In January 2022, a Canadian AI startup BenchSci Analytics Inc. received USD 50 million that has Moderna Inc., Bristol Myers Squibb Co., AstraZeneca Plc, and Sanofi as its clients. In December 2021, the startup of the Montreal-based renowned Mila Artificial Intelligence (AI) Research Institute, Valence Discovery announced the funding of USD 8.5 million to support drug discovery efforts.

Thus, all the factors such as high disease prevalence, increasing focus on clinical research as well as drug development are expected to contribute to the growing demand for AI in the drug discovery process in North America during the forecast period.

AI in Drug Discovery Market key players:

Some of the key market players operating in the AI in Drug Discovery Market include IBM Corporation, Numedii Inc, Deep Genomics, NVIDIA Corporation, Atomwise Inc, Cloud Pharmaceuticals Inc, Alphabet Inc (DeepMind), Insilico Medicine, BenevolentAI, Exscientia, Cyclia, Valo Health, Owkin Inc, Verge Genomics, BioSymetrics, and others.

Recent Developmental Activities in the AI in Drug Discovery Market:

• In May 2024, Sanofi, Formation Bio, and Open AI entered into a collaboration to develop AI-powered software aimed at accelerating drug development and enhancing the efficiency of bringing new medicines to patients. Together, they aimed to create customized, purpose-built solutions designed to optimize various stages of the drug development lifecycle, thereby streamlining processes and improving outcomes in pharmaceutical innovation.
• In July 2023, Insilico Medicine announced the first drug discovered and designed by generative AI into Phase II clinical trials with patients. This lead program, for a potentially first-in-class pan-fibrotic inhibitor known as INS018_055 - is Insilico's moonshot drug one that demonstrates beyond a doubt the validity of Insilico's end-to-end AI drug discovery.
• In January 2022, Sanofi entered into a research collaboration with Exscientia, which uses artificial intelligence to discover new drug candidates that could be worth up to USD 5.2 billion for the latter company.
• In December 2021, Insilico Medicine announced the start of the world's first Phase I clinical trial of a drug developed from scratch using AI. Its end-to-end platform applies AI to biology for target discovery, and to chemistry for drug design. The AI-designed drug is a small-molecule inhibitor and is developed for the treatment of idiopathic pulmonary fibrosis.
• In November 2021, Alphabet Inc. announced the launch of a new company- Isomorphic Laboratories which will leverage AI for drug discovery.

Key Takeaways from the AI in Drug Discovery Market Report Study

• Market size analysis for current AI in drug discovery market size (2023), and market forecast for 6 years (2024 to 2030)
• Top key product/services developments, mergers, acquisitions, partnerships, and joint ventures happened for the last 3 years
• Key companies dominating the AI in drug discovery market.
• Various opportunities available for the other competitors in the AI in drug discovery market space.
• What are the top-performing segments in 2023? How these segments will perform in 2030?
• Which are the top-performing regions and countries in the current AI in drug discovery market scenario?
• Which are the regions and countries where companies should have concentrated on opportunities for AI in drug discovery market growth in the coming future?

Target audience who can be benefited from this AI in Drug Discovery Market Report Study

• AI in drug discovery product providers
• Research organizations and consulting companies
• AI in drug discovery-related organizations, associations, forums, and other alliances
• Government and corporate offices
• Start-up companies, venture capitalists, and private equity firms
• Distributors and traders dealing in AI in drug discovery
• Various end-users who want to know more about the AI in drug discovery market and latest technological developments in the AI in drug discovery market.

Frequently Asked Questions for the AI in Drug Discovery Market:

1. What is AI in drug discovery market?

Artificial intelligence (AI) in drug discovery is the utilization of advanced computing techniques such as machine learning, artificial neural networks, and natural language processing to process large amounts of data to help with target, lead identification, and other required inputs for drug discovery and development.

2. What is the market for AI in drug discovery market?

The AI in drug discovery market is estimated to advance at a CAGR of 37.67% during the forecast period from 2024 to 2030.

3. What are the drivers for the AI in drug discovery market?

The AI in drug discovery market is witnessing positive market growth owing to factors such as the rising prevalence of various diseases across the globe which have necessitated the need for faster development of highly safe and efficacious drugs. Moreover, the realization of the advantages of AI in the pharmaceutical sector is further motivating pharma companies and institutes to further invest in drug research and development. Additionally, the extensive partnerships and collaborations between public and private entities and both national and international levels are further expected to boost the AI in drug discovery market during the forecast period from 2024 to 2030.

4. Who are the key players operating in the AI in drug discovery market?

Some of the key market players operating in AI in the drug discovery market include IBM Corporation, Numedii Inc, Deep Genomics, NVIDIA Corporation, Atomwise Inc, Cloud Pharmaceuticals Inc, Alphabet Inc (DeepMind), Insilico Medicine, BenevolentAI, Exscientia, Cyclia, Valo Health, Owkin Inc, Verge Genomics, BioSymetrics, and others.

5. Which region has the highest share in the AI in drug discovery market?

Among all the regions, North America is estimated to hold a significant revenue share in the AI in Drug Discovery Market. This can be ascribed to the presence of a large patient population associated with various diseases including cancers, and neurological disorders which in turn drive the demand for various drugs with minimal side effects. Moreover, the extensive focus on clinical research and the presence of key players in the region from both the pharmaceutical as well as the technology domains further help in the growth of the North America AI in drug discovery market during the forecast period from 2024 to 2030.

Table of Contents

1. AI in Drug Discovery Market Report Introduction

• 1.1. Scope of the Study
• 1.2. Market Segmentation
• 1.3. Market Assumption

2. AI in Drug Discovery Market Executive Summary

• 2.1. Market at Glance

3. Competitive Landscape

4. Regulatory Analysis

• 4.1. The United States
• 4.2. Europe
• 4.3. Japan
• 4.4. China

5. AI in Drug Discovery Market Key Factors Analysis

• 5.1. AI in Drug Discovery Market Drivers
  • 5.1.1. Increasing prevalence of chronic diseases
  • 5.1.2. Growing interest in leveraging artificial intelligence in drug development owing to its advantages
  • 5.1.3. Growing collaboration between public and private entities operating in pharma and AI domains
• 5.2. AI in Drug Discovery Market Restraints and Challenges
  • 5.2.1. Knowledge gaps between biologics/chemists and AI scientists
  • 5.2.2. Limitations of traditional machine learning tools in data handling
• 5.3. AI in Drug Discovery Market Opportunities
  • 5.3.1. Development of safer cancer therapies with minimal side-effects

6. AI in Drug Discovery Market Porter's Five Forces Analysis

• 6.1. Bargaining Power of Suppliers
• 6.2. Bargaining Power of Consumers
• 6.3. Threat of New Entrants
• 6.4. Threat of Substitutes
• 6.5. Competitive Rivalry

7. AI in Drug Discovery Market Assessment

• 7.1. By Type
  • 7.1.1. De Novo Drug Design and Optimization
  • 7.1.2. Preclinical Development
  • 7.1.3. Others
• 7.2. By Application
  • 7.2.1. Oncology
  • 7.2.2. Cardiovascular
  • 7.2.3. Infectious Diseases
  • 7.2.4. Others
• 7.3. By End-User
  • 7.3.1. Pharmaceutical and Biotechnology Companies
  • 7.3.2. Contract Research Organizations (CROs)
  • 7.3.3. Others
• 7.4. By Geography
  • 7.4.1. North America
    • 7.4.1.1. United States AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.1.2. Canada AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.1.3. Mexico AI in Drug Discovery Market Size in USD million (2021-2030)
  • 7.4.2. Europe
    • 7.4.2.1. France AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.2.2. Germany AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.2.3. United Kingdom AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.2.4. Italy AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.2.5. Spain AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.2.6. Rest of Europe AI in Drug Discovery Market Size in USD million (2021-2030)
  • 7.4.3. Asia-Pacific
    • 7.4.3.1. China AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.3.2. Japan AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.3.3. India AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.3.4. Australia AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.3.5. South Korea AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.3.6. Rest of Asia-Pacific AI in Drug Discovery Market Size in USD million (2021-2030)
  • 7.4.4. Rest of the World (RoW)
    • 7.4.4.1. Middle East AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.4.2. Africa AI in Drug Discovery Market Size in USD million (2021-2030)
    • 7.4.4.3. South America AI in Drug Discovery Market Size in USD million (2021-2030)

8. AI in Drug Discovery Market Company and Product Profiles

• 8.1. IBM Corporation
  • 8.1.1. Company Overview
  • 8.1.2. Company Snapshot
  • 8.1.3. Financial Overview
  • 8.1.4. Product Listing
  • 8.1.5. Entropy
• 8.2. Numedii Inc
  • 8.2.1. Company Overview
  • 8.2.2. Company Snapshot
  • 8.2.3. Financial Overview
  • 8.2.4. Product Listing
  • 8.2.5. Entropy
• 8.3. Deep Genomics
  • 8.3.1. Company Overview
  • 8.3.2. Company Snapshot
  • 8.3.3. Financial Overview
  • 8.3.4. Product Listing
  • 8.3.5. Entropy
• 8.4. NVIDIA Corporation
  • 8.4.1. Company Overview
  • 8.4.2. Company Snapshot
  • 8.4.3. Financial Overview
  • 8.4.4. Product Listing
  • 8.4.5. Entropy
• 8.5. Atomwise Inc
  • 8.5.1. Company Overview
  • 8.5.2. Company Snapshot
  • 8.5.3. Financial Overview
  • 8.5.4. Product Listing
  • 8.5.5. Entropy
• 8.6. Cloud Pharmaceuticals Inc
  • 8.6.1. Company Overview
  • 8.6.2. Company Snapshot
  • 8.6.3. Financial Overview
  • 8.6.4. Product Listing
  • 8.6.5. Entropy
• 8.7. Alphabet Inc (DeepMind)
  • 8.7.1. Company Overview
  • 8.7.2. Company Snapshot
  • 8.7.3. Financial Overview
  • 8.7.4. Product Listing
  • 8.7.5. Entropy
• 8.8. Insilico Medicine
  • 8.8.1. Company Overview
  • 8.8.2. Company Snapshot
  • 8.8.3. Financial Overview
  • 8.8.4. Product Listing
  • 8.8.5. Entropy
• 8.9. BenevolentAI
  • 8.9.1. Company Overview
  • 8.9.2. Company Snapshot
  • 8.9.3. Financial Overview
  • 8.9.4. Product Listing
  • 8.9.5. Entropy
• 8.10. Exscientia
  • 8.10.1. Company Overview
  • 8.10.2. Company Snapshot
  • 8.10.3. Financial Overview
  • 8.10.4. Product Listing
  • 8.10.5. Entropy
• 8.11. Cyclia
  • 8.11.1. Company Overview
  • 8.11.2. Company Snapshot
  • 8.11.3. Financial Overview
  • 8.11.4. Product Listing
  • 8.11.5. Entropy
• 8.12. Valo Health
  • 8.12.1. Company Overview
  • 8.12.2. Company Snapshot
  • 8.12.3. Financial Overview
  • 8.12.4. Product Listing
  • 8.12.5. Entropy
• 8.13. Owkin Inc
  • 8.13.1. Company Overview
  • 8.13.2. Company Snapshot
  • 8.13.3. Financial Overview
  • 8.13.4. Product Listing
  • 8.13.5. Entropy
• 8.14. Verge Genomics
  • 8.14.1. Company Overview
  • 8.14.2. Company Snapshot
  • 8.14.3. Financial Overview
  • 8.14.4. Product Listing
  • 8.14.5. Entropy
• 8.15. BioSymetrics
  • 8.15.1. Company Overview
  • 8.15.2. Company Snapshot
  • 8.15.3. Financial Overview
  • 8.15.4. Product Listing
  • 8.15.5. Entropy

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10. Project Approach

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