Market Overview

The Artificial Intelligence in Drug Discovery Market size was valued at around 1.48 billion in 2023 and is expected to reach a value of USD 15.50 billion by 2032, at a CAGR of  29.8 % over the forecast period (2024–2032).

AI-solution demand for drug discovery is growing with respect to the required new drug treatments, enhanced capacity in life science manufacturing, and technological innovations. AI technologies such as machine and deep learning are being utilized on multiple steps involved in drug discovery, from lead compound screening through success rate estimation in clinical trials. Additionally, rising investments, financing, and the emergence of startups working on AI-driven solutions are anticipated to fuel market growth. For example, in April 2024, Xaira Therapeutics revealed that it had raised $1 million in funding for developing drug discovery.

In addition, the implementation of AI solutions to the clinical trial process has transformed the sector by overcoming possible barriers, increasing efficiency and accuracy, and lowering cycle times. The sophisticated method is becoming more fashionable among the parties in the life sciences sector as more recognize its advantages. Statistics from Clinical Trials Arena in 2021 reflect a sharp increase in strategic alliances and partnerships among AI-based drug discovery companies and pharma organizations, with these partnerships growing from 4 in 2015 to 27 in 2020. The trend reflects the growing use of AI in the simplification of drug discovery and development processes.

Biomedical and clinical research digitalization is the way forward when it comes to adopting AI solutions. The enormous datasets created from drug discovery workflows, including molecule screening stages and preclinical tests, are driving AI-powered solution adoption. Such titanic datasets complicate analysis by scientists. AI solutions can expedite screening and lower turnaround times.

The market expansion is fuelled by the existence of numerous options, including data mining and personalization features, for implementing AI solutions in drug discovery activities. Greater precision is attained through combining deep learning and machine learning algorithms within AI systems that identify the molecule binding attributes of drugs. Additionally, the use of sophisticated technologies like electronic data capture (EDC) helps manufacturers enhance patient data management and minimize monitoring expenses. Process mistakes can be reduced using electronic Clinical Outcome Assessment (e-COA) in AI solutions. Advanced analytics have been recently incorporated into such AI solutions, which help stakeholders with data mining, patient recruitment, and medical & clinical records management.

Of the various stages of a clinical trial study, the preclinical testing phase contributes the most to revenue loss with less return. Implementation of AI solutions can streamline the preclinical testing phase to reduce costs. AI models correctly interpret human physiological response without experimental expenditure. Robust regulations regarding clinical trial studies outlined by regulatory bodies across the globe are expected to increase the demand for AI solutions in drug discovery procedures. Conversely, government officials in some developed and emerging markets are taking positive steps to enhance the penetration of AI solutions and the volume of clinical trials.

Market Drivers

Advancements in Big Data and Computational Power

• Improved data analysis, genomics research, and artificial intelligence simulation are revolutionizing drug discovery more quickly and accurately. AI uses huge genomics, proteomics, and clinical trials databases to zero in on candidate drug compounds and biomarkers with quicker speed than conventional techniques. Machine learning models scrutinize intricate biological interactions, predicting drug efficacy and toxicity with better accuracy. Genomics helps tailor medicine using the detection of genetic variation that is associated with disease so that AI can develop personalized treatment. AI simulations also speed up the discovery of drugs by mimicking the interaction of molecules, thereby decreasing dependence on expensive and time-consuming laboratory testing. Apart from simplifying drug manufacturing, such technologies reduce failure in clinical trials, ultimately bringing about quicker and cheaper treatment of a range of diseases.

Collaborations Between Pharma Companies and AI Startups

• Leading pharma companies are now more often partnering with AI companies to enhance innovation and accelerate drug discovery. These collaborations leverage AI's ability to screen vast biomedical data sets, identify potential medicine candidates, and optimize clinical trial designs.  Through the prediction of molecular interactions and target identification, AI-based systems help pharmaceutical companies reduce research costs and timelines. Pharmaceutical companies like Pfizer, Novartis, and Merck have collaborated with AI startups to enhance drug discovery pipelines, particularly in oncology, neurology, and rare diseases. Such arrangements also enable precision medicine to be developed by coupling AI with genomics and patient data. With the advancement of AI technology, such partnerships are bound to revolutionize drug discovery, making it more effective and likely to bring new treatments to the market at a quicker pace.

Market Opportunity

Personalized Medicine Development

• Precision medicine is being revolutionized by AI-based approaches that tailor medicines to an individual patient's specific genetic blueprint, resulting in more effective and targeted treatments. AI can be used to make precision medicines possible through its capacity to search huge genomic databases and identify genetic variations and disease-linked biomarkers. A machine learning approach reduces side effects and reduces trial-and-error prescribing based on genetic variation to determine how patients would react to a medicine. AI also facilitates available medicines' reuse by examining their genetic signatures and comparing them against new disease drug signatures. AI speeds the production of immunotherapies, orphan diseases' orphan medicines, and patient-specific cancer cures for biopharma firms.  AI is improving treatment and improving medicine by combining patient data and genes with AI, and it is becoming cheaper daily.

Expansion in Rare Disease Drug Discovery

• AI is transforming the rare disease drug discovery process by discovering new drugs in the absence of limited research data. AI addresses these challenges by analyzing vast biological datasets, including genomes, proteomics, and electronic health records. Conventional methods are hampered by small patient cohorts and insufficient clinical data. Machine learning algorithms forecast the aetiology of diseases, identify potential drug targets, and detect trends in gene changes. By analyzing the disease profile of orphans versus that of available drugs, AI accelerates drug repurposing too, with time and cost savings.  Benevolent AI and Insilco Medicine are two firms that utilize AI to identify lead compounds for trials. By automating data processing and predictive modelling, AI makes orphan disease research possible, bringing patients with conditions for which effective treatment does not yet exist a new glimmer of hope.

Market Restraining Factors

Limited AI Expertise in Pharma Industry

• Lack of experts in artificial intelligence as well as pharmaceutical sciences is one of the main hindrances in applying AI in pharmaceutical development. Biological processes, pharmacology experts, and machine learning techniques are needed in the use of AI for developing medicines. While AI specialists are more prevalent in the field of computing, the pharmaceutical company has always banked on biologists and chemists. Since companies do not have experts who have specialization in huge domains, lack of expertise is hindering AI application in the development of drugs. Research centres and universities are establishing interdisciplinary programs that combine pharmacology, bioinformatics, and AI to address this. The development of AI-based drug development relies on expanding such training programs and promoting collaboration between pharmaceutical professionals and AI researchers.

Segmentation Analysis

The market scope is segmented because of by Technology, by Application, by Drug Type, by End-User.

• By Technology

Based on the Technology of the market is segmented into Machine Learning (ML), Natural Language Processing (NLP), Deep Learning, Other AI Technologies.

Machine learning (ML) propels artificial intelligence (AI)-enabled drug discovery, using computers to dig through enormous data sets to make predictions about possible therapeutic ideas. All these are enabled by machine learning (ML), particularly deep learning, when it comes to chemical property prediction, structure-activity relationship (SAR) prediction, and virtual screening. For instance, Benevolent AI applies ML to determine candidate drug targets by scanning biomedical literature and genomic data.

Natural Language Processing (NLP) helps in information extraction from scientific literature, clinical trials reports, and patents. NLP helps researchers stay up to date with new drug candidates and regulatory updates. For instance, IBM Watson uses NLP to analyse medical texts and suggest potential treatments for diseases like cancer.

Deep Learning, a branch of ML, is particularly effective in processing intricate biological information. It makes protein structure predictions, speeds drug screening, and improves compound optimization. DeepMind's AlphaFold revolutionized the prediction of protein structures, allowing for quicker drug discovery. Other AI technologies, including generative AI, robotics, and quantum computing, complement drug design and synthesis. Generative adversarial networks (GANs) generate new molecular structures, and robot labs that use artificial intelligence synthesize drugs. GANs are employed by Insilco Medicine to design completely new drug molecules, shortening the development time and cost. Machine learning, especially deep learning, is leading the charge, propelling innovation and speeding up pharmaceutical breakthroughs.

• By Application

Based on the Application of the market is segmented into Target Identification & Validation, Drug Discovery & Design, Preclinical & Clinical Testing, Other Applications.

Drug design and drug discovery propel the process of drug development from target identification to clinical trials. Target identification & validation is the process of discovering biological molecules (targets) engaged in disease states and establishing that they play a part in disease causation. It becomes possible to produce therapeutic actions by regulating the target. Drug discovery & design is then pursued, where drugs are screened or designed to bind to the established target efficiently. Computational methods, high-throughput screening, and AI-based modelling complement this stage. Preclinical & clinical trials then check for efficacy & safety. Preclinical testing and animal experimentation fall under laboratory and animal experimentation, while clinical trials progress through phases to test safety, dose, and effectiveness in human beings. Other uses involve personalized therapy, off-label use of approved drugs, and drug delivery system optimization. For instance, oncology saw breast cancer treatment in HER2-positive disease start from the identification of the target within the HER2 receptor, after which trastuzumab (Herceptin) came about, proving to successfully get through preclinical and clinical trials prior to licensure. It is a methodical process by which new medication becomes effective as well as safe before patients' reception.

• Regional Snapshots

By region, Insights into the markets in North America, Europe, Asia-Pacific, Latin America and MEA are provided by the study. North America artificial intelligence in drug discovery market held the largest market share of 57.7% in 2023 because of heavy investments in healthcare technology and heavy collaboration among pharmaceutical firms and technology giants. The presence of high-quality research institutions in the region along with a supportive regulatory environment further encourages innovation. Organizations are now adopting AI to mechanize drug discovery processes, decrease costs, and speed up the launch of new therapies into the market.

Artificial Intelligence in Drug Discovery U.S. Market held the largest market share in 2023. The U.S. is at the forefront of AI drug discovery, with major pharmaceutical and technology firms investing heavily in AI technologies. The FDA's proactive strategy for regulatory standards for AI in healthcare encourages innovation while ensuring safety and efficacy. The US market is characterized by a extremely high degree of M&A, where companies are seeking to complement AI capabilities for enhancing competitiveness in drug discovery. In February 2024, for instance, Ginkgo Bioworks bought Reverie Labs' AI/ML tools and infrastructure for large-scale AI foundation models to reinforce its AI/ML-powered discovery services and speed up the development of next-generation biological foundation models.

Europe artificial intelligence in drug discovery market is likely to increase considerably during the forecast period. Europe is a prominent region in the AI in drug discovery market, with considerable contributions from nations such as Germany and the UK. The region has a robust research infrastructure and positive regulatory policies that Favor the integration of AI in healthcare. European businesses are the leaders in AI adoption for drug discovery, aiming at personalized medicine and sophisticated data analysis to refine drug development methodologies. Joint work among academia, industry, and government institutions is pushing major advances in this sector.

Artificial intelligence drug discovery market in UK is anticipated to expand strongly during the forecast period. UK has many AI-led initiatives focused on transforming drug discovery. Government backing, through programs such as the Industrial Strategy Challenge Fund, and partnership between industry and academia are driving factors for the use of AI technologies in the UK.

Asia Pacific artificial intelligence in drug discovery market is likely to expand at the highest CAGR through the end of 2030. The region is also experiencing the rapid expansion of the AI in drug discovery market due to countries such as China and India. Organizations in the Asia Pacific are establishing AI technologies for speeding up drug discovery and development aimed at enhancing clinical trial efficiency and fulfilling unmet medical requirements. For example, Fujitsuand RIKEN jointly created AI technology for drug discovery in October 2023 by using generative AI to predict protein structural changes. This innovation combines AI algorithms with electron microscope images with a view to streamlining drug development processes while maximizing costs.

Japanese drug discovery market for artificial intelligence is becoming an emerging player with a vision for integrating. AI is being invested in by Japanese companies to streamline drug discovery operations and create tailored treatments. Digital health transformation momentum given by the government and the strong industry-research institution partnership are driving growth in AI-powered drug discovery in Japan. In addition, the other market participants' entry into the Japan are also anticipated to boost the market growth. For example, in September 2021, an Israeli startup CytoReason has entered Japan's third-largest pharmaceutical market through a partnership with Summit Pharmaceuticals International, the Sumitomo Corporation's drug research and development arm.

China artificial intelligence in drug discovery market is a leading adopter of AI in Asia-Pacific, investing heavily in healthcare AI and biotech. Numerous Chinese pharmaceutical companies are more frequently collaborating with AI companies to propel drug development activities. Such collaborations viewed as instrumental in boosting China's pharma industry, which has been recording lower growth rates. China's research organizations on contract are already benefiting from AI technologies, which are creating international interest in new molecules. As reported by The Economist Newspaper Limited on March 2024, China spent more than USD 1.26 Million on AI-enabled drug discovery in 2021, emphasizing its focus on maximizing the use of cutting-edge technologies in healthcare development.

List of Companies Profiled

• IBM
• Exscientia
• Insilico Medicine
• GNS Healthcare (In January 2023, the company Rebranded as Aitia)
• Google (DeepMind)
• BenevolentAI
• BioSymetrics, Inc.
• Berg Health (In January 2023, Berg Health acquired by BPGbio Inc.)
• Atomwise Inc.
• insitro
• CYCLICA (In May 2023, CYCLICA acquired by Recursion).

Recent Developments

In July 2024, Exscientia made a new partnership with Amazon Web Services (AWS) to leverage AWS's machine learning (ML) and artificial intelligence (AI) services in order to strengthen its end-to-end drug discovery and automation platform.

In May 2024, Google DeepMind launched the third iteration of its AlphaFold AI model, which is meant to accelerate drug design and disease targeting. The new release enables scientists at DeepMind and Isomorphic Labs to describe all molecules, including human DNA.

In April 2024, Xaira Therapeutics, which is an artificial intelligence drug discovery and development firm, raised more than USD 1 Million in a co-funding round with ARCH Venture Partners and Foresite Labs. Xaira Therapeutics applies machine learning, data generation models, and therapeutic product development to develop drugs for traditionally difficult drug targets

In December 2023, MilliporeSigma, a business of Merck in life science, launched AIDDISON, a cutting-edge drug discovery software. It is meant to integrate seamlessly the design of virtual molecules and realistic manufacturability. It utilizes the Synthia retrosynthesis software API to improve efficiency and feasibility in drug development operations.

In May 2023, Google launched two new AI-driven solutions that are intended to help biotech and pharmaceutical firms speed up drug discovery and enhance precision medicine. These solutions aim to make the process of bringing new drugs to the U.S. market faster and cheaper. Cerevel Therapeutics, Pfizer, and Colossal Biosciences are some of the early customers to adopt these solutions.

Report Coverage

The report will cover the qualitative and quantitative data on the global Artificial Intelligence in Drug Discovery Market. The qualitative data includes latest trends, market players analysis, market drivers, market opportunity, and many others. Also, the report quantitative data includes market size for every region, country, and segments according to your requirements. We can also provide customize report in every industry vertical.

Report Scope and Segmentations

Global AI in Drug Discovery Market Regional Analysis

North America accounted for the highest xx% market share in terms of revenue in the AI in Drug Discovery market and is expected to expand at a CAGR of xx% during the forecast period. This growth can be attributed to the growing adoption of AI in Drug Discovery. The market in APAC is expected to witness significant growth and is expected to register a CAGR of xx% over upcoming years, because of the presence of key AI in Drug Discovery companies in economies such as Japan and China.

The objective of the report is to present comprehensive analysis of Global AI in Drug Discovery Market including all the stakeholders of the industry. The past and current status of the industry with forecasted market size and trends are presented in the report with the analysis of complicated data in simple language.

AI in Drug Discovery Market Report is also available for below Regions and Country Please Ask for that

North America

• U.S.
• Canada
• Mexico

Europe

• Switzerland
• Belgium
• Germany
• France
• U.K.
• Italy
• Spain
• Sweden
• Netherland
• Turkey
• Rest of Europe

Asia-Pacific

• India
• Australia
• Philippines
• Singapore
• South Korea
• Japan
• China
• Malaysia
• Thailand
• Indonesia
• Rest Of APAC

Latin America

• Mexico
• Argentina
• Peru
• Colombia
• Brazil
• Rest of South America

Middle East and Africa

• Saudi Arabia
• UAE
• Egypt
• South Africa
• Rest Of MEA

Points Covered in the Report

• The points that are discussed within the report are the major market players that are involved in the market such as market players, raw material suppliers, equipment suppliers, end users, traders, distributors and etc.
• The complete profile of the companies is mentioned. And the capacity, production, price, revenue, cost, gross, gross margin, sales volume, sales revenue, consumption, growth rate, import, export, supply, future strategies, and the technological developments that they are making are also included within the report. This report analysed 5 years data history and forecast.
• The growth factors of the market are discussed in detail wherein the different end users of the market are explained in detail.
• Data and information by market player, by region, by type, by application and etc., and custom research can be added according to specific requirements.
• The report contains the SWOT analysis of the market. Finally, the report contains the conclusion part where the opinions of the industrial experts are included.

Key Questions

• How much the global AI in Drug Discovery Market valued?
• Which region has the largest share in 2024 for the global AI in Drug Discovery Market?
• What are the driving factors for the market?
• Which is the leading segment in the global market?
• What are the major players in the market?

Research Scope of AI in Drug Discovery Market

• Historic year: 2019-2022
• Base year: 2023
• Forecast: 2024 to 2032
• Representation of Market revenue in USD Billion

AI in Drug Discovery Market Trends: Market key trends which include Increased Competition and Continuous Innovations Trends: