Artificial Intelligence In Drug Discovery Market Size, Share, Opportunities, And Trends By Offering (Software, Services), By Technology (Machine Learning, Deep Learning, Natural Language Processing (NLP), Other AI Technologies), By Therapeutic Area (Oncology, Neurology, Cardiovascular Diseases, Infectious Diseases, Others), By Application (Target Identification and Validation, Hit-to-Lead Identification, Lead Optimization, Drug Repurposing, Others), By End-User (Pharmaceutical Companies, Biotechnology Companies, Contract Research Organizations (CROs), Research Institutes, Others), And By Geography - Forecasts From 2023 To 2028

The artificial intelligence in drug discovery market was valued at US$328.424 million in 2021.

The artificial intelligence (AI) in drug discovery market growth is rapidly expanding and has numerous applications. AI integration is helping drug development, tissue engineering, and regenerative medicine. By making use of powerful algorithms and machine learning, AI systems can quickly and accurately analyze huge amounts of data, such as the results of clinical trials and molecular structures. By expediting the identification of viable therapeutic candidates, this reduces the amount of time and money spent on drug discovery. In addition, AI makes it easier to create medicines that are more individualized and efficient. With its promise to spur innovation and enhance patient outcomes, AI in Drug Discovery is destined to alter pharmaceutical research and development.

Introduction:

In the pharmaceutical industry, Artificial Intelligence (AI) in Drug Discovery market growth has emerged as a disruptive force. By utilizing powerful algorithms and machine-learning methods, AI is revolutionizing the process of identifying and developing novel medications. By analyzing enormous amounts of data, such as molecular structures, genomes, and the results of clinical trials, AI algorithms  can discover potential medication candidates with remarkable speed and accuracy.

In addition to making, it possible to develop medicines that are more effective and individualized, this method has the potential to cut down on the amount of time and money spent on drug discovery. With its promise to accelerate innovation and improve patient outcomes, Artificial Intelligence in the Drug Discovery market size is expected to alter pharmaceutical research and development.

Key Players in Artificial Intelligence in Drug Discovery Market:

• IBM Watson Health: The cognitive computing system IBM Watson for Drug Discovery makes use of artificial intelligence (AI) to speed up drug research and development processes. It aids researchers in analyzing huge amounts of data, such as patents, clinical trials, and scientific literature, to identify potential therapeutic targets and optimize potential compounds.
• BenevolentAI: They explain how graph-driven AI platforms can be used to combine and analyze biological data in order to find new drug targets and develop novel therapeutics. Their platform combines knowledge of biology and chemicals with AI algorithms to generate recommendations for drug discovery and repurposing.
• Atomwise: Using AI-powered virtual screening technologies, Atomwise predicts the binding affinity of tiny compounds to target proteins. Their AtomNet technology makes it easier to find new drug candidates for a variety of conditions because it allows for the rapid screening of millions of molecules.
• Exscientia: Using AI-driven methods, they specialize in creating novel medicinal compounds. Machine learning algorithms are used in their Centaur Chemist platform to predict the best compounds for synthesis and testing, speeding up drug discovery.
• Berg Health: Using AI and machine learning algorithms, they analyse patient data, genomes, and molecular information for drug development and precision medicine. Their Interrogative Biology platform enables the creation of individualized therapeutics and sheds light on the causes of diseases.

Drives:

• Increasing adoption of AI and machine learning technologies:

A greater appreciation for the potential of AI and machine learning methods in drug discovery is evident. Modern technologies make it possible to quickly analyse complex data, which improves decision-making, identifies new drug targets, and optimizes drug candidates, ultimately enhancing the drug development process.

• Advancements in bioinformatics and computational biology:

Advances in computational biology and bioinformatics have made significant contributions to drug development. Advanced algorithms, computational models, and tools for the efficient processing of biological data in fields like genomics and proteomics are among these innovations. By leveraging these breakthroughs, researchers can gain significant insight into disease processes, identify potential drug targets, and accelerate the discovery and development of novel treatments.

• Potential to identify rare and undruggable targets:

AI has the potential to be used in drug development because it can find rare and undruggable targets that cannot be solved by traditional methods. AI algorithms are capable of analysing intricate data and locating novel therapeutic targets, making it possible to develop medicines for diseases that were once thought to be difficult to treat. This ability opens up new possibilities for the treatment of a wide range of diseases and expands the scope of drug development.

• Growing demand for accelerated drug discovery processes:

The increased demand for expedited drug discovery methods is driven by the desire for quicker and more efficient treatment development. Researchers can save time and money by simplifying various phases of drug discovery, such as identifying targets and optimizing leads, with the assistance of AI technologies like predictive modeling and machine learning. The need to fill medical gaps and provide patients with novel medicines as soon as possible is the driving force behind this demand.

The artificial intelligence in drug discovery market is expanding at a steady pace in the forecast period.

The market for artificial intelligence in drug discovery is segmented by technology, application, end-user, offerings, therapeutic area, and geography. Technology is further segmented into machine learning, deep learning, natural language processing, and other AI technologies. The therapeutic area is further segmented into oncology, neurology, cardiovascular diseases, and infectious diseases.

North America is the largest region in artificial intelligence (AI) in drug discovery market.

The artificial intelligence (AI) in drug discovery market share is mainly controlled by North America. There are many possible causes for this. To begin, AI technology adoption and deployment are facilitated by North America's robust healthcare infrastructure, superior research facilities, and significant pharmaceutical industry. Additionally, the region is well-served by prominent AI technology suppliers and pharmaceutical companies involved in AI-driven drug development. In addition, the favorable regulatory framework, government efforts to support them, and high level of R&D activity in North America all contribute to the expansion of AI in Drug Discovery. North America is the market leader because of all of these factors.

Market Developments:

• In May 2023, BenevolentAI, a pioneer in combining cutting-edge Artificial Intelligence (AI) and science to expedite biopharma discovery and development, claims that AstraZeneca revealed fresh preclinical results on an AI-generated target discovered in their ongoing collaboration. At the ATS International Conference in 2023, the findings were presented.
• In April 2022, the beginning of a joint AI research project to combat dengue, one of the world's most prevalent infectious diseases, was announced by BenevolentAI, an established clinical-stage artificial intelligence (AI) allowed drug discovery organization, and Drugs for Neglected Disease Initiative (DNDi), a nonprofit research and development (R&D) organization focusing on neglected diseases.
• In August 2022, with a $1.2 billion biobucks collaboration with Atomwise of San Francisco, Sanofi is pushing the boundaries of AI-powered medication development.

 

Artificial Intelligence In Drug Discovery Market Scope:

 

Report Metric	Details
Market Size Value in 2021	US$328.424 million
Growth Rate	CAGR during the forecast period
Base Year	2021
Forecast Period	2023 – 2028
Forecast Unit (Value)	USD Million
Segments Covered	Offering Technology Therapeutic Area Application End-User Geography
Companies Covered	IBM Corporation Microsoft Corporation Alphabet Inc. (Google) NVIDIA Corporation Atomwise, Inc.
Regions Covered	North America, South America, Europe, Middle East and Africa, Asia Pacific
Customization Scope	Free report customization with purchase

 

Key Market Segment:

• BY OFFERING
  • Software
  • Services
• BY TECHNOLOGY
  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Other AI Technologies       
• BY THERAPEUTIC AREA
  • Oncology
  • Neurology
  • Cardiovascular Diseases
  • Infectious Diseases
  • Others
• BY APPLICATION
  • Target Identification and Validation
  • Hit-to-Lead Identification
  • Lead Optimization
  • Drug Repurposing
  • Others
• BY END-USER
  • Pharmaceutical Companies
  • Biotechnology Companies
  • Contract Research Organizations (CROs)
  • Research Institutes
  • Others
• BY GEOGRAPHY
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Others
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain    
    • Others
  • Middle East and Africa
    • Saudi Arabia
    • UAE
    • Others
  • Asia Pacific
    • Japan
    • China
    • India
    • South Korea
    • Indonesia
    • Taiwan
    • Others

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1. INTRODUCTION

1.1. Market Overview

1.2. Market Definition

1.3. Scope of the Study

1.4. Market Segmentation

1.5. Currency

1.6. Assumptions

1.7. Base and Forecast Years Timeline

2. RESEARCH METHODOLOGY  

2.1. Research Data

2.2.  Research Process

3. EXECUTIVE SUMMARY

3.1. Research Highlights

4. MARKET DYNAMICS

4.1. Market Drivers

4.2. Market Restraints

4.3. Porters Five Forces Analysis

4.3.1. Bargaining Power of Suppliers

4.3.2. Bargaining Power of Buyers

4.3.3. Threat of New Entrants

4.3.4. Threat of Substitutes

4.3.5. Competitive Rivalry in the Industry

4.4. Industry Value Chain Analysis

5. ARTIFICIAL INTELLIGENCE (AI) IN THE DRUG DISCOVERY MARKET, BY OFFERING

5.1. Introduction

5.2. Software

5.3. Services 

6. ARTIFICIAL INTELLIGENCE (AI) IN THE DRUG DISCOVERY MARKET, BY TECHNOLOGY

6.1. Introduction

6.2. Machine Learning

6.3. Deep Learning

6.4. Natural Language Processing (NLP)

6.5. Other AI Technologies        

7. ARTIFICIAL INTELLIGENCE (AI) IN THE DRUG DISCOVERY MARKET BY THERAPEUTIC AREA

7.1.  Introduction

7.2. Oncology

7.3. Neurology

7.4. Cardiovascular Diseases

7.5. Infectious Diseases

7.6. Others 

8. ARTIFICIAL INTELLIGENCE (AI) IN THE DRUG DISCOVERY MARKET BY APPLICATION

8.1.  Introduction

8.2. Target Identification and Validation

8.3. Hit-to-Lead Identification

8.4. Lead Optimization

8.5. Drug Repurposing

8.6. Others 

9. ARTIFICIAL INTELLIGENCE (AI) IN THE DRUG DISCOVERY MARKET BY END-USER

9.1.  Introduction

9.2. Pharmaceutical Companies

9.3. Biotechnology Companies

9.4. Contract Research Organizations (CROs)

9.5. Research Institutes

9.6. Others 

10. ARTIFICIAL INTELLIGENCE (AI) IN THE DRUG DISCOVERY MARKET BY GEOGRAPHY

10.1. Introduction

10.2. North America

10.2.1. United States

10.2.2. Canada

10.2.3. Mexico

10.3. South America

10.3.1. Brazil

10.3.2. Argentina

10.3.3. Others

10.4. Europe

10.4.1. United Kingdom

10.4.2. Germany

10.4.3. France

10.4.4. Italy

10.4.5. Spain

10.4.6. Others

10.5. Middle East and Africa

10.5.1. Saudi Arabia

10.5.2. UAE

10.5.3. Others

10.6. Asia Pacific

10.6.1. Japan

10.6.2. China

10.6.3. India

10.6.4. South Korea

10.6.5. Indonesia 

10.6.6. Taiwan

10.6.7. Others

11. COMPETITIVE ENVIRONMENT AND ANALYSIS

11.1. Major Players and Strategy Analysis

11.2.  Market Share Analysis

11.3. Mergers, Acquisitions, Agreements, and Collaborations

12. COMPANY PROFILES

12.1. IBM Corporation

12.2. Microsoft Corporation

12.3. Alphabet Inc. (Google)

12.4. NVIDIA Corporation

12.5. Atomwise, Inc.

12.6. BenevolentAI

12.7. Exscientia Ltd.

12.8. Insilico Medicine

12.9. Cyclica Inc.

12.10. Cloud Pharmaceuticals, Inc.

IBM Corporation

Microsoft Corporation

Alphabet Inc. (Google)

NVIDIA Corporation

Atomwise, Inc.

BenevolentAI

Exscientia Ltd.

Insilico Medicine

Cyclica Inc.

Cloud Pharmaceuticals, Inc.