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AI Drug Discovery Market

ID: MRFR/Pharma/7918-CR
200 Pages
Rahul Gotadki
July 2023

Artificial Intelligence in Drug Discovery Market Research Report: Size, Share, Trend Analysis By Applications (Target Identification, Lead Optimization, Drug Repurposing, Clinical Trials, Preclinical Testing), By Technology (Machine Learning, Natural Language Processing, Deep Learning, Knowledge Graphs, Robotic Process Automation), By End Use (Pharmaceutical Companies, Biotechnology Firms, Research Institutions, Academic Institutions), By Workflow (Data Mining, Predictive Modeling, Clinical Data Management, Assay Development) - Growth Outlook & Industry Forecast 2025 To 2035

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Summary Table of Contents Segmentation Download PDF

AI Drug Discovery Market Summary

As per Market Research Future analysis, the Artificial Intelligence (AI) in Drug Discovery Market was estimated at 0.93 USD Billion in 2024. The AI in Drug Discovery industry is projected to grow from 1.172 USD Billion in 2025 to 11.82 USD Billion by 2035, exhibiting a compound annual growth rate (CAGR) of 26% during the forecast period 2025 - 2035

Key Market Trends & Highlights

The Artificial Intelligence in Drug Discovery Market is poised for substantial growth driven by technological advancements and increasing collaboration across sectors.

  • North America remains the largest market for AI in drug discovery, reflecting robust investment and innovation in biotechnology.
  • The Asia-Pacific region is emerging as the fastest-growing market, fueled by increasing demand for advanced healthcare solutions.
  • Lead optimization continues to dominate the market, while drug repurposing is rapidly gaining traction as a cost-effective strategy.
  • Rising demand for personalized medicine and advancements in machine learning algorithms are key drivers propelling market expansion.

Market Size & Forecast

2024 Market Size 0.93 (USD Billion)
2035 Market Size 11.82 (USD Billion)
CAGR (2025 - 2035) 26.0%

Major Players

IBM (US), Google (US), Microsoft (US), Bristol-Myers Squibb (US), Insilico Medicine (HK), Atomwise (US), Exscientia (GB), Recursion Pharmaceuticals (US), Zebra Medical Vision (IL)

Our Impact
Enabled $4.3B Revenue Impact for Fortune 500 and Leading Multinationals
Partnering with 2000+ Global Organizations Each Year
30K+ Citations by Top-Tier Firms in the Industry

AI Drug Discovery Market Trends

The Artificial Intelligence (AI) in Drug Discovery Market is currently experiencing a transformative phase, driven by advancements in computational technologies and the increasing complexity of biological systems. This market appears to be characterized by a growing integration of machine learning algorithms and data analytics, which facilitate the identification of potential drug candidates more efficiently than traditional methods. As pharmaceutical companies seek to reduce development timelines and costs, the adoption of AI technologies seems to be accelerating, leading to a paradigm shift in how drugs are discovered and developed. Furthermore, collaborations between tech firms and biopharmaceutical companies are likely to enhance innovation, fostering an environment where novel therapeutic solutions can emerge more rapidly. In addition, regulatory bodies are beginning to recognize the potential of AI in drug discovery, which may lead to more streamlined approval processes for AI-driven solutions. This evolving landscape suggests that the Artificial Intelligence (AI) in Drug Discovery Market is poised for substantial growth, as stakeholders increasingly acknowledge the value of integrating AI into their research and development pipelines. The future may hold even greater advancements, as ongoing research continues to unlock new possibilities in drug discovery, potentially revolutionizing the industry as a whole.

Increased Collaboration Between Sectors

The trend of collaboration between technology companies and pharmaceutical firms is becoming more pronounced. This partnership aims to leverage AI capabilities to enhance drug discovery processes, combining expertise in software development with deep knowledge of biological sciences.

Enhanced Data Utilization

There is a growing emphasis on the utilization of vast datasets in drug discovery. AI technologies are increasingly employed to analyze complex biological data, which may lead to more accurate predictions of drug efficacy and safety.

Regulatory Adaptation

Regulatory agencies are adapting to the rise of AI in drug discovery. This trend indicates a potential shift towards more favorable guidelines that could facilitate the approval of AI-driven methodologies, thereby accelerating the development of new therapies.

AI Drug Discovery Market Drivers

Increased Investment in Biotechnology

The surge in investment within the biotechnology sector is propelling the Artificial Intelligence (AI) in Drug Discovery Market forward. Venture capital funding and government grants are increasingly directed towards AI-driven biotech firms, facilitating the development of innovative drug discovery solutions. In 2023, investments in AI-focused biotech companies reached approximately USD 1.2 billion, underscoring the growing confidence in AI's potential to revolutionize drug development. This influx of capital not only accelerates research and development but also fosters collaboration between tech companies and pharmaceutical firms, enhancing the overall landscape of drug discovery.

Regulatory Support for AI Integration

Regulatory bodies are increasingly recognizing the potential of AI in drug discovery, providing support that drives the Artificial Intelligence (AI) in Drug Discovery Market. Initiatives aimed at establishing guidelines for the use of AI in clinical trials and drug approval processes are emerging. This regulatory support not only enhances the credibility of AI-driven solutions but also encourages pharmaceutical companies to adopt these technologies. As regulations evolve to accommodate AI innovations, the market is expected to benefit from increased trust and acceptance, facilitating the integration of AI into mainstream drug discovery practices.

Rising Demand for Personalized Medicine

The increasing emphasis on personalized medicine is a key driver in the Artificial Intelligence (AI) in Drug Discovery Market. As healthcare shifts towards tailored treatments, AI technologies are being leveraged to analyze vast datasets, including genetic information, to identify potential drug candidates that are more effective for specific patient populations. This trend is reflected in the projected growth of the market, which is expected to reach USD 3.5 billion by 2026. The ability of AI to predict patient responses to drugs enhances the efficiency of drug development processes, thereby reducing time and costs associated with bringing new therapies to market.

Advancements in Machine Learning Algorithms

Recent advancements in machine learning algorithms are significantly influencing the Artificial Intelligence (AI) in Drug Discovery Market. These algorithms enable researchers to process and analyze complex biological data more efficiently, leading to the identification of novel drug candidates. For instance, deep learning techniques have shown promise in predicting molecular interactions and optimizing drug design. The market is anticipated to grow at a compound annual growth rate (CAGR) of 40% from 2023 to 2030, driven by these technological innovations. As machine learning continues to evolve, its applications in drug discovery are likely to expand, further enhancing the industry's capabilities.

Growing Need for Cost-Effective Drug Development

The pressing need for cost-effective drug development is a significant driver in the Artificial Intelligence (AI) in Drug Discovery Market. Traditional drug discovery processes are often lengthy and expensive, with high failure rates. AI technologies offer solutions to streamline these processes, potentially reducing costs by up to 30%. By utilizing predictive analytics and simulations, AI can identify promising drug candidates earlier in the development pipeline, thereby minimizing resource expenditure. As pharmaceutical companies seek to optimize their R&D budgets, the adoption of AI in drug discovery is likely to increase, further driving market growth.

Market Segment Insights

By Application: Lead Optimization (Largest) vs. Drug Repurposing (Fastest-Growing)

In the Artificial Intelligence in Drug Discovery Market, the application segment is prominently represented by categories such as Target Identification, Lead Optimization, Drug Repurposing, Clinical Trials, and Preclinical Testing. Among these, Lead Optimization holds the largest market share, showcasing its crucial role in refining drug candidates. Drug Repurposing is gaining traction as a significant player, reflecting its innovative approach to utilizing existing drugs for new therapeutic purposes.

Lead Optimization (Dominant) vs. Drug Repurposing (Emerging)

Lead Optimization serves as a dominant application in AI-driven drug discovery, acting as a pivotal phase where computational techniques refine promising drug candidates into viable products. It leverages extensive databases and predictive modeling to enhance selectivity and efficacy, which reduces time and resources. In contrast, Drug Repurposing emerges as a rapidly growing segment, utilizing AI to analyze existing medications for new uses. This approach benefits from lower costs and shortened timelines, appealing to biotech firms under budget constraints and urgent market demands. Both applications reflect trends in efficiency and innovation within the pharmaceutical industry.

By Technology: Machine Learning (Largest) vs. Deep Learning (Fastest-Growing)

The Artificial Intelligence (AI) in Drug Discovery Market showcases a competitive landscape dominated by Machine Learning, which captures the largest market share among the various technology segments. Natural Language Processing and Knowledge Graphs follow, showing significant contributions to overall market dynamics. Meanwhile, Robotic Process Automation, although smaller in terms of market share, plays an essential role in enhancing efficiency in drug discovery processes, thus solidifying its presence in the market.

Technology: Machine Learning (Dominant) vs. Deep Learning (Emerging)

Machine Learning stands out as the dominant technology in the AI-driven drug discovery landscape due to its proven capabilities in analyzing vast datasets and extracting crucial insights. It enables pharmaceutical companies to expedite the drug development process by identifying potential candidates and predicting their efficacy. On the other hand, Deep Learning, which is rapidly emerging, leverages neural networks to improve accuracy in drug target identification and molecular prediction. Its adaptive nature allows it to learn from complex data structures, making it essential for innovative approaches in drug design. Together, these technologies are reshaping the traditional drug discovery paradigms.

By End Use: Pharmaceutical Companies (Largest) vs. Biotechnology Firms (Fastest-Growing)

The Artificial Intelligence (AI) in Drug Discovery Market exhibits a diverse segmentation by end use, prominently featuring Pharmaceutical Companies, Biotechnology Firms, Research Institutions, and Academic Institutions. Among these, Pharmaceutical Companies hold the largest market share, leveraging advanced AI technologies to enhance drug discovery processes, streamline research, and improve efficiency. Following closely, Biotechnology Firms represent a significant portion of the market as they accelerate innovation through AI and focus on personalized medicine, making them vital players in this dynamic landscape.

Pharmaceutical Companies (Dominant) vs. Biotechnology Firms (Emerging)

Pharmaceutical Companies are currently the dominant segment in the AI in Drug Discovery Market, adopting cutting-edge technologies to optimize drug development timelines and reduce costs. These companies utilize AI for various applications, such as predicting drug interactions and analyzing complex biological data, which facilitates faster decision-making in drug research. In contrast, Biotechnology Firms are emerging as key players, particularly in the realm of bespoke therapies. With a heavy focus on precision medicine and gene editing, they harness AI to design more effective therapies tailored to individual patient needs, positioning themselves uniquely in the evolving market.

By Workflow: Data Mining (Largest) vs. Predictive Modeling (Fastest-Growing)

In the Artificial Intelligence (AI) in Drug Discovery Market, Data Mining is currently the largest segment, accounting for a significant portion of the overall market share. This segment focuses on extracting valuable insights from vast datasets, enabling researchers to make data-driven decisions. Closely following is Predictive Modeling, which is witnessing rapid growth as it enhances the ability to forecast outcomes based on historical data, allowing for more effective drug discovery processes. The growth trends for these segments are driven by the increasing volume of biological data generated from various sources, including clinical trials and genomic studies. As pharmaceutical companies strive for efficiency in drug development, the adoption of AI-driven Data Mining and Predictive Modeling tools continues to rise. This trend is further supported by advancements in machine learning algorithms that improve the accuracy and speed of data analysis, making these workflows indispensable in modern drug discovery.

Data Mining (Dominant) vs. Clinical Data Management (Emerging)

In the context of the Artificial Intelligence (AI) in Drug Discovery Market, Data Mining is recognized as the dominant workflow due to its pivotal role in synthesizing complex datasets into actionable insights. It facilitates the identification of potential drug candidates by uncovering patterns and relationships within the data. Meanwhile, Clinical Data Management is emerging as an important segment, focusing on maintaining the quality and integrity of clinical trial data. This workflow is increasingly incorporating AI tools to streamline the handling and analysis of clinical data, reducing errors and improving the efficiency of drug development. The synergy between these two workflows highlights the evolving landscape of AI in drug discovery, where Data Mining leads while Clinical Data Management positions itself as an essential component of the process.

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Regional Insights

North America : Innovation and Investment Hub

North America is the largest market for AI in drug discovery, holding approximately 45% of the global share. The region benefits from significant investments in technology and healthcare, driven by a robust pharmaceutical industry and supportive regulatory frameworks. The demand for faster drug development processes and personalized medicine is propelling growth, with government initiatives promoting AI integration in healthcare. The United States is the dominant player, home to major companies like IBM, Google, and Bristol-Myers Squibb. The competitive landscape is characterized by a mix of established pharmaceutical giants and innovative startups. Canada is also emerging as a key player, leveraging its strong research institutions and favorable policies to foster AI advancements in drug discovery.

Europe : Regulatory Support and Growth

Europe is the second-largest market for AI in drug discovery, accounting for around 30% of the global market share. The region is witnessing a surge in demand for AI technologies, driven by increasing investments in healthcare innovation and supportive regulatory frameworks. The European Medicines Agency is actively promoting the use of AI in drug development, which is expected to further accelerate market growth. Leading countries include Germany, the UK, and France, which are at the forefront of AI research and development. The competitive landscape features a mix of established pharmaceutical companies and innovative tech firms, such as Exscientia and Insilico Medicine. Collaborative efforts between academia and industry are fostering advancements in AI applications for drug discovery.

Asia-Pacific : Rapid Growth and Innovation

Asia-Pacific is rapidly emerging as a significant player in the AI in drug discovery market, holding approximately 20% of the global share. The region is driven by increasing investments in healthcare technology, a growing number of biotech firms, and supportive government initiatives aimed at enhancing drug development processes. Countries like China and India are leading the charge, with a focus on leveraging AI to address healthcare challenges and improve patient outcomes. China is particularly notable for its aggressive investment in AI and biotechnology, with companies like Atomwise and Recursion Pharmaceuticals making strides in drug discovery. India is also gaining traction, supported by a burgeoning startup ecosystem and collaborations with global firms. The competitive landscape is characterized by a mix of local and international players, fostering innovation and growth in the sector.

Middle East and Africa : Emerging Market Potential

The Middle East and Africa region is in the early stages of developing its AI in drug discovery market, currently holding about 5% of the global share. The market is driven by increasing healthcare investments, a growing focus on research and development, and the need for innovative solutions to address local health challenges. Governments are beginning to recognize the potential of AI in enhancing drug discovery processes, which is expected to catalyze growth in the coming years. Countries like South Africa and the UAE are leading the way, with initiatives aimed at fostering AI adoption in healthcare. The competitive landscape is still developing, with a mix of local startups and international firms exploring opportunities in the region. Collaborative efforts between governments, academia, and industry are essential for driving innovation and establishing a robust AI ecosystem in drug discovery.

AI Drug Discovery Market Regional Image

Key Players and Competitive Insights

The global market for artificial intelligence in drug development has emerged as a crucial priority for prominent pharmaceutical and technology businesses aiming to optimize their drug discovery processes and reduce time to market. This sector leverages advanced machine learning algorithms and data analytics, significantly improving the efficiency of identifying potential drug candidates. As the demand for customized medicine and new medicines escalates, competition in this sector intensifies, emphasizing strategic collaborations, technological improvements, and the development of intellectual property.Companies are currently exploring the integration of AI for medication identification, as well as for the optimization of clinical trials and post-market surveillance.
The environment features significant investments in research and development, focused on developing advanced AI platforms that can predict medication interactions and side effects, with the goal of optimizing each phase of the drug development process.
Novartis stands out in the Artificial Intelligence in Drug Discovery Market due to its commitment to innovation and technological adoption. With a robust research and development pipeline, Novartis has integrated AI into various aspects of its drug discovery activities, allowing for more efficient screening and optimizing molecular drug design. It’s strategic collaborations with tech firms and academic institutions fortify its market presence, enabling Novartis to leverage state-of-the-art AI solutions.
Novartis's strengths lie in its extensive portfolio covering a diverse range of therapeutic areas and its capability to utilize AI in repurposing existing drugs, potentially speeding up the drug discovery process. This forward-thinking approach combined with Novartis's established industry reputation solidifies its competitive edge in leveraging AI technologies within drug discovery for global applications.
Atomwise is another key player in the Artificial Intelligence in Drug Discovery Market, recognized for its innovative use of AI in drug design and development. The company’s proprietary technology utilizes deep learning algorithms to predict the effectiveness of potential drug molecules, substantially accelerating the initial phases of drug discovery. Atomwise has made significant strides in creating strategic partnerships with various pharmaceutical companies and research institutions globally, allowing for extensive application of its technology in diverse therapeutic areas.
The company's strength lies in its unique AI platform, which offers efficient virtual screening services, yielding a high success rate in drug candidates' identification. Atomwise has also engaged in notable collaborations and mergers that have bolstered its market presence, enhancing its product offerings and capabilities in drug discovery. This focus on AI and strategic expansion allows Atomwise to maintain a competitive position in the rapidly evolving landscape of drug discovery, driving innovation and efficiency on a global scale.

Key Companies in the AI Drug Discovery Market include

Industry Developments

DEC 2025 - AI-driven drug discovery continues to gain momentum as pharmaceutical companies adopt machine-learning platforms for target identification, lead optimization, and predictive modeling. Several AI-designed molecules have progressed into clinical stages, accelerating timelines and reducing R&D cost. Strategic collaborations between biotech companies and cloud-AI vendors are rising sharply. Regulators are beginning to define clearer frameworks for AI-assisted drug development, supporting industry-wide adoption.

The global market for artificial intelligence in drug discovery is experiencing significant growth, as leading pharmaceutical firms such as Novartis, Pfizer, and AstraZeneca utilize AI to enhance the efficiency of drug development. Atomwise continues to be a prominent entity, recognized for its AtomNet® platform that facilitates the virtual screening of billions of compounds, greatly enhancing the process of early-stage drug identification. Other firms like Insilico Medicine and DeepMind (through Isomorphic Labs) have secured significant funding, highlighting the industry's innovative capabilities and growing investor trust.

Exscientia’s collaboration with Bristol Myers Squibb, announced in May 2021, represented a significant milestone in their partnerships. The agreement, worth more than $1.2 billion, focuses on leveraging AI to enhance the efficiency of drug development across various therapeutic targets. The collaboration has successfully produced a first-in-human trial for a PKC-theta inhibitor (EXS4318), highlighting the tangible benefits of AI-driven innovation in clinical development.

Despite the prevailing market optimism, it is crucial to clarify that IBM did not acquire any AI drug discovery firm in August 2023, contrary to certain reports. In 2022, the organization restructured its earlier healthcare AI initiatives, leading to the formation of Merative. Overall, the advancements in AI within the drug discovery sector are set for ongoing growth, fueled by strategic partnerships, evolving platforms, and a significant focus on technology integration.

Future Outlook

AI Drug Discovery Market Future Outlook

The Artificial Intelligence in Drug Discovery Market is projected to grow at a 26.0% CAGR from 2025 to 2035, driven by advancements in machine learning, data analytics, and increased R&D investments.

New opportunities lie in:

  • Integration of AI-driven predictive analytics in clinical trials
  • Development of AI platforms for personalized medicine
  • Partnerships with biotech firms for AI-enhanced drug design

By 2035, the market is expected to be a pivotal component of pharmaceutical innovation.

Market Segmentation

AI Drug Discovery Market End Use Outlook

  • Pharmaceutical Companies
  • Biotechnology Firms
  • Research Institutions
  • Academic Institutions

AI Drug Discovery Market Workflow Outlook

  • Data Mining
  • Predictive Modeling
  • Clinical Data Management
  • Assay Development

AI Drug Discovery Market Technology Outlook

  • Machine Learning
  • Natural Language Processing
  • Deep Learning
  • Knowledge Graphs
  • Robotic Process Automation

AI Drug Discovery Market Application Outlook

  • Target Identification
  • Lead Optimization
  • Drug Repurposing
  • Clinical Trials
  • Preclinical Testing

Report Scope

MARKET SIZE 2024 0.93(USD Billion)
MARKET SIZE 2025 1.172(USD Billion)
MARKET SIZE 2035 11.82(USD Billion)
COMPOUND ANNUAL GROWTH RATE (CAGR) 26.0% (2025 - 2035)
REPORT COVERAGE Revenue Forecast, Competitive Landscape, Growth Factors, and Trends
BASE YEAR 2024
Market Forecast Period 2025 - 2035
Historical Data 2019 - 2024
Market Forecast Units USD Billion
Key Companies Profiled IBM (US), Google (US), Microsoft (US), Bristol-Myers Squibb (US), Insilico Medicine (HK), Atomwise (US), Exscientia (GB), Recursion Pharmaceuticals (US), Zebra Medical Vision (IL)
Segments Covered Applications, Technology, End Use, Workflow
Key Market Opportunities Integration of advanced machine learning algorithms enhances drug candidate identification and accelerates Research and Development processes.
Key Market Dynamics Rising integration of Artificial Intelligence in drug discovery enhances efficiency and accelerates the Research and Development process.
Countries Covered North America, Europe, APAC, South America, MEA

Market Highlights

Author
Rahul Gotadki
Assistant Manager

He holds an experience of about 7+ years in Market Research and Business Consulting, working under the spectrum of Life Sciences and Healthcare domains. Rahul conceptualizes and implements a scalable business strategy and provides strategic leadership to the clients. His expertise lies in market estimation, competitive intelligence, pipeline analysis, customer assessment, etc. In addition to the above, his other responsibility includes strategic tracking of high growth markets & advising clients on the potential areas of focus they could direct their business initiatives

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FAQs

What is the projected market valuation for the Artificial Intelligence (AI) in Drug Discovery Market by 2035?

The projected market valuation for the AI in Drug Discovery Market is expected to reach 11.82 USD Billion by 2035.

What was the market valuation for the AI in Drug Discovery Market in 2024?

The market valuation for the AI in Drug Discovery Market was 0.93 USD Billion in 2024.

What is the expected compound annual growth rate (CAGR) for the AI in Drug Discovery Market from 2025 to 2035?

The expected CAGR for the AI in Drug Discovery Market during the forecast period 2025 - 2035 is 26.0%.

Which companies are considered key players in the AI in Drug Discovery Market?

Key players in the AI in Drug Discovery Market include IBM, Google, Microsoft, Bristol-Myers Squibb, Insilico Medicine, Atomwise, Exscientia, Recursion Pharmaceuticals, and Zebra Medical Vision.

What are the main application segments of the AI in Drug Discovery Market?

The main application segments include Target Identification, Lead Optimization, Drug Repurposing, Clinical Trials, and Preclinical Testing.

How much is the Lead Optimization segment projected to be valued by 2035?

The Lead Optimization segment is projected to be valued at 3.0 USD Billion by 2035.

What technologies are driving the AI in Drug Discovery Market?

Driving technologies in the AI in Drug Discovery Market include Machine Learning, Natural Language Processing, Deep Learning, Knowledge Graphs, and Robotic Process Automation.

What is the projected valuation for the Machine Learning segment by 2035?

The Machine Learning segment is projected to reach a valuation of 3.8 USD Billion by 2035.

Which end-use sectors are contributing to the AI in Drug Discovery Market?

The end-use sectors contributing to the market include Pharmaceutical Companies, Biotechnology Firms, Research Institutions, and Academic Institutions.

What is the expected valuation for the Assay Development workflow segment by 2035?

The Assay Development workflow segment is expected to be valued at 3.82 USD Billion by 2035.

Research Approach

Research Methodology on AI in Drug Discovery Market

1. Introduction

The published research report aims to explore the prospects of Artificial Intelligence (AI) in Drug Discovery and Development processes. AI has revolutionized numerous sectors, and Drug Discovery and Development is no exception. AI has the potential to make Drug Discovery and Development processes faster, more accurate and more predictable. This is the main motivation of this research to explore the valuable opportunities and challenges arising from the use of AI technologies in Drug Discovery and Development processes.

For the application of AI in Drug Discovery, the research explores two tools: Monte-Carlo Simulations and Machine Learning. Therefore, the research seeks to gain an in-depth understanding of the AI Drug Discovery and Development market and provide insights into the strategies used by the leading players.

2. Research Objectives

- To assess the rate of AI-enabled drug discovery and development activities

- To provide an in-depth analysis of the AI-driven drug discovery and development market.

- To identify the drivers and restraints that have an impact on AI-driven drug discovery and development activities.

- To predict the future of the AI-driven Drug Discovery and Development industry

- To assess the opportunities and challenges associated with AI-enabled drug discovery and development processes

- To provide insights into the strategies adopted by the leading players in the AI Drug Discovery and Development market

3. Data Sources

The primary data sources for this research are the annual reports of the leading players in the AI Drug Discovery and Development market and other related reports from various organizations, including the World Health Organization (WHO), the US Food and Drug Administration (FDA), the European Medicines Agency (EMA) etc. Secondary data sources include published books, journals, online articles, and reports on AI Drug Discovery and Development. The data sources are selected in order to provide the most accurate and up-to-date information.

4. Research Methodologies

The research follows a qualitative approach using a combination of desk research and secondary data collection. Desk research involves the collection of data from published reports, experts, books, journals and online sources. The secondary data used in the research is further analyzed and interpreted by the experts in order to identify trends and patterns in the dataset.

The research methodology includes three main phases:

Phase 1: Identification of AI-driven drugs and drug discovery opportunities.

Phase 2: Assessing the drivers and restraints of the AI-driven Drug Discovery and Development industry.

Phase 3: Analysis of the strategies adopted by the leading players in the AI Drug Discovery and Development market.

5. Data Analysis Techniques

In order to analyze the collected data, various quantitative and qualitative data analysis techniques are used. Qualitative data analysis involves the use of Interviews and Focus Group Discussions with experts in the field of AI Drug Discovery and Development, as well as with representatives from the leading players in the market. Quantitative data analysis involves the use of SPSS software to analyse the collected data and generate insights into the trends and patterns in the dataset.

6. Validity and Reliability

The data gathered through this research is validated and cross-checked through telephone/video interviews with experts in the AI Drug Discovery and Development market and with representatives from the leading players in the industry. The data is also verified through the primary and secondary sources used in the research. This ensures the accuracy and reliability of the data and results.

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