Generative AI in Drug Discovery Market Size, Share and Trends Analysis, By Technology (Deep learning, Machine learning, Molecular docking, Quantum computing, and Reinforcement learning), By Application, By End-Use, and By Region, Forecast 2025-2032

Global Generative AI in Drug Discovery Market Overview and Key Insights:

The global Generative AI in Drug Discovery market size reached USD 216.7 Million in 2024 and is expected to register a revenue CAGR of 28.7% during the forecast period. Gen AI is changing the pharmaceutical sector by modernizing drug discovery, clinical trials, and production. AI technology companies are driving this innovation by developing powerful algorithms that forecast drug interactions, enhance trial designs, and improve patient results.

Market Drivers:

Increasing prevalence of chronic disorders is a key driver of revenue growth in the global Generative AI in Drug Discovery market. According to the Center of Disease Control and Prevention (CDC), antibiotic resistance is one of the most serious dangers to public health. Every year, more than 2.8 million people in the United States become infected with antibiotic-resistant bacteria, resulting in the deaths of over 35,000 people. Many more people die from antibiotic-resistant illnesses. Furthermore, according to the AR Threats Report, when C. diff—a bacteria that is not normally resistant but can cause fatal diarrhea and relates to antibiotic use—is included, the total number of infections and deaths in the United States approaches 3 million.

Demand for cost effective drug discovery is driving the demand for Generative AI in Drug Discovery market. Generative AI has a major impact on all stages of the drug discovery process, from early research to post-market surveillance, improving efficiency and effectiveness. AI helps to optimize lead drugs by proposing chemical alterations to improve medicinal efficacy and safety. This process refines drug candidates by improving their pharmacological properties and minimizing potential side effects. Generative AI accelerates each stage of the drug discovery process while also improving decision-making, resulting in more efficient and effective drug development. This comprehensive integration of AI significantly impacts healthcare by improving drug quality and patient outcomes.

Market Opportunity:

Increase in demand for personalized medicine acts as an opportunity for the global Generative AI in Drug Discovery market. Generative AI can help in drug discovery, reducing costs by up to 70%. It can provide tailored treatment plans by assessing patient data, genetic information, medical histories, and lifestyle factors, so enhancing treatment outcomes and quality of life.

On December 2024, Vizuro LLC, a leading inventor in multimodal causal AI technology, and the Development Center for Biotechnology (DCB) have signed a Memorandum of Understanding (MOU) to jointly drive innovation and growth in the biotechnology and pharmaceutical industries. The goal of this collaboration is to create a next-generation AI computational platform that will accelerate drug discovery breakthroughs and precision medicine adoption. The collaboration lays the path for ground-breaking developments in the global biotech industry.

Recent Trends:

Recent trends include the integration of quantum computing, optimization of clinical trials, and increasing investment.

Generative AI has quickly progressed from a novel notion to a driving factor in rethinking clinical trial procedures. Its responsibilities are broad, impacting protocol development, identification of appropriate patient groups, trial site selection, adherence to regulatory requirements, patient retention tactics, clinical documentation management, and adverse event monitoring.

On 2024, Intelligent Medical Objects, based in Rosemont, Illinois, created SEETrials, a way for motivating OpenAI’s big language model GPT-4 to extract safety and efficacy information from clinical trial abstracts. This allows trial designers to rapidly see how other researchers constructed trials and what the results were.

Restraints & Challenges:

High initial investment poses a significant hurdle to the implementation of Generative AI in Drug Discovery market. Setting up the computational infrastructure, including GPUs and cloud-based systems is expensive. Hiring experts in AI, bioinformatics, and computational biology also adds to the cost. Furthermore, Gen AI requires large high-quality datasets. Obtaining, cleaning, and curating these datasets, especially in life sciences, is resource intensive.

Technology Segment Insights and Analysis:

Based on the Technology, the global Generative AI in Drug Discovery market is segmented into Deep learning, Machine learning, Molecular docking, Quantum computing, and Reinforcement learning.

Machine learning segment contributed largest share in 2024. In drug discovery, computational intelligence gives various approaches for evaluating and learning, as well as explains how such pharmaceutical was identified with AI for identifying multiple pharmaceuticals in a programmed and integrated manner. As a result, numerous pharmaceutical businesses have shown a greater desire to contribute to technology and resources for obtaining accurate outcomes in drug discovery.

On October 2024, AION Labs, the first-of-its-kind venture studio driving the use of AI technologies and computational science to solve therapeutic challenges, has announced the launch of ProPhet, a new firm that uses artificial intelligence to identify active small compounds for drug discovery. ProPhet was founded following AION Labs’ startup challenge, which focused on AI-powered drug discovery of new high-affinity small compounds.

Quantum computing segment is expected to have the highest growth rate throughout the forecasted period. Drug development is becoming an increasingly expensive and time-consuming endeavor for biopharma businesses, but the rise of quantum computing is predicted to alter the early drug discovery process for organizations trying to make it more cost-effective and efficient. Quantum computing, when combined with standard high-performance computers, can improve early drug development through exact physical simulations and the design of novel compounds using data-driven machine learning and generative modeling.

On October 2024, D-Wave Quantum Inc., a leader in quantum computing systems, software, and services and the world’s first commercial supplier of quantum computers, and Japan Tobacco Inc.’s pharmaceutical division announced plans to collaborate on a joint proof-of-concept project leveraging quantum computing technology and artificial intelligence in the drug discovery process, dubbed “Quantum AI-driven Drug Discovery.”

Application Segment Insights and Analysis:

Based on the Application, the global Generative AI in Drug Discovery market is segmented into Molecule design, Antibody design & development, De novo drug design, Precision drug discovery, Toxicity Prediction, and Others.

Molecule design segment contributed largest share in 2024. Generative AI models provide a more effective method for navigating the enormous chemical space and constructing unique molecular structures with desired features. Currently, a variety of strategies, including VAEs, GANs, RNNs, evolutionary algorithms, and reinforcement learning, are employed to create compounds with desirable ADMET features. Generative AI systems, with their ability to simultaneously optimize many features of a molecule, can assist in identifying candidates with the most balanced profile in terms of efficacy, safety, and other pharmacological factors.

On November 2023, Terray Therapeutics, a biotechnology business that combines scale experimentation and generative AI to increase the speed, cost, and success rate of small molecule drug discovery and development, has announced an equity investment from NVentures, NVIDIA’s venture capital division. Terray will use NVIDIA DGX Cloud to create the world’s most comprehensive chemistry foundation models for small compounds, part of which will be made available via the NVIDIA BioNeMo cloud service for generative AI in drug discovery.

Antibody design & development segment is expected to have the highest growth rate throughout the forecasted period. The ongoing growth of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches has greatly aided computational antibody discovery as a supplement to traditional lab-based methods. Generative antibody design can dramatically improve the speed, quality, and efficiency of antibody design, allowing for more targeted and potent treatment modalities and the generation of unique target-specific antibodies that are outside the scope of standard design procedures.

On November 2023, Boehringer Ingelheim and IBM have announced an agreement that would allow Boehringer to employ IBM’s foundation model technology to identify unique candidate antibodies for the development of effective medicines. This work is also the most recent example of IBM’s use of generative AI and foundation models to speed the discovery and development of new biologics and small chemicals.

End-Use Segment Insights and Analysis:

Based on the End-Use, the global Generative AI in Drug Discovery market is segmented into Pharmaceutical & Biotechnology Companies, Contract Research Organizations (CROs), and Academic and Research Institutions.

Pharmaceutical & Biotechnology Companies segment contributed largest share in 2024. Generative AI effortlessly integrates with pharmaceutical businesses, improving crucial stages of the drug development process. Its strengths in data processing and pattern recognition enable more effective screening of large chemical libraries, identifying compounds with potential therapeutic benefits faster than traditional methods. Pharma companies may use Generative AI to not only accelerate the discovery and development of novel pharmaceuticals, but also improve precision and lower the costs associated with bringing successful treatments to market with an AI-enhanced customer experience.

On September 2024, Gilead Sciences, Inc. and Genesis Therapeutics, Inc. announced a strategic collaboration to discover and develop innovative small molecule medicines across numerous targets. Genesis is pioneering generative and predictive artificial intelligence (AI) tools to assist develop treatments for difficult targets. This collaboration will use Genesis’ field-leading AI technology, GEMS (Genesis Exploration of Molecular Space), to help generate and optimize compounds for Gilead-selected targets.

Contract Research Organizations (CROs) segment is expected to have the highest growth rate throughout the forecasted period. Artificial intelligence (AI) is being integrated into clinical research to improve the efficacy of clinical trials and significantly contribute to the operations of contract research organizations (CROs). AI algorithms can predict future adverse occurrences, improve our understanding of the toxicity of certain chemical compounds, and assist reduce the potential side effects of pharmaceuticals and therapies. The ability to predict using AI-based models enables CROs to process and identify patterns in huge volumes of data, tailoring real-time updates and responding beyond human capabilities.

On March 2024, Elsevier, a global leader in information and data analytics, has reached a multi-year collaboration with Iktos, a firm that specializes in artificial intelligence for innovative medication discovery. The cooperation will enhance Elsevier’s flagship chemistry solution, Reaxys, by integrating high-quality chemistry data with Iktos’ synthetic planning AI technology, accelerating chemistry research for pharmaceutical businesses. New prediction models will be introduced to enable use cases such as retrosynthesis, synthetic accessibility, and reaction-based analysis. The new tools will be offered through Reaxys’ user-friendly interface and Application Programming Interfaces (APIs), which will speed up research for synthetic, medicinal, computational, and process chemistry teams at pharmaceutical, agrochemical, and contract research organizations (CROs).

Geographical Outlook:

North America is registered highest market share in Generative AI in Drug Discovery market in 2024 driven by increasing prevalence of chronic disorders and technological advancement of computational power. According to the American Cancer Society, in 2024, the US is anticipated to have 611,720 cancer-related fatalities, which equates to around 1,680 each day. Cancer is the second leading cause of death in the United States, only surpassed by heart disease. In 2024, the US is anticipated to have 611,720 cancer-related fatalities, which equates to around 1,680 each day. Cancer is the second leading cause of death in the United States, only surpassed by heart disease.

On March 2024, NVIDIA has announced more than two dozen new microservices, allowing healthcare companies around the world to benefit from the most recent developments in generative AI from any location and cloud. The new NVIDIA healthcare microservices package combines optimized NVIDIA NIM AI models and workflows with industry-standard APIs (application programming interfaces) that serve as building blocks for developing and deploying cloud-native applications. They provide enhanced imaging, natural language, and speech recognition, as well as the synthesis, prediction, and simulation of digital biology.

Asia Pacific is registered the fastest growth rate during the forecasted period. Artificial intelligence (AI) holds great promise for Japan, with the Japanese Government’s AI Strategic Council recognizing the country’s strong affinity for generative AI, which is due in part to its high levels of research and technology, as well as the potential of generative AI to address issues such as the decline in the working age population and the need for digitalisation. Doctors make thousands of imaging diagnoses each day to detect cancer cases, but approximately 20% of early-stage stomach malignancies are still missed.

On October 2023, Fujitsu Limited and the RIKEN Center for Computational Science’s HPC- and AI-driven Drug Development Platform Division announced in January 2023 that they had developed an AI drug discovery technology that can predict structural changes of proteins from electron microscope images as a 3D density map over a wide range using generative AI. Fujitsu and RIKEN collaborated to develop a generative AI technology that accurately estimates the various forms of a target protein’s conformation and their possible proportions from many electron microscopy projection images, as well as a technology that predicts conformational changes in the target protein based on the estimated proportions.

Europe is to register a considerable market share in Generative AI in Drug Discovery market in 2024. Generative AI is transforming the healthcare industry by automating, augmenting, and accelerating various work processes. Its ability to recognize learning patterns from large sets of unstructured data is transforming healthcare in unprecedented ways. However, there are disparities in AI development and implementation between EU countries, particularly in medical AI.

On July 2024, Exscientia plc announced that it will expand its collaboration with Amazon Web technologies (AWS) by leveraging the cloud provider’s artificial intelligence (AI) and machine learning (ML) technologies to power its platform for end-to-end drug discovery and automation. Exscientia’s platform employs generative AI models, as well as the scale and flexibility of AWS, to safely, swiftly, and efficiently build drug candidates with the goal of better targeting specific diseases and patients, hence expediting early drug development at a reduced cost.

Competition Analysis:

The global Generative AI in Drug Discovery market is characterized by a fragmented structure, with several players competing across various segments and regions. List of major players included in the Generative AI in Drug Discovery market report are:

• BioSymetrics
• Merck KGaA
• NVIDIA Corporation
• BenevolentAI
• Insilico Medicine
• Atomwise Inc.
• XtalPi Inc
• Cyclica Inc
• Variational AI Inc.
• Numerate Inc
• Absci Corp.
• BioStrand BV
• Innodata Inc.
• Recursion Pharmaceuticals, Inc

Strategic Developments in Global Generative AI in Drug Discovery Market:

• In December 2024, SoftServe, a leading provider of IT consulting and digital services, announced the launch of its newest Generative AI (Gen AI) product, Drug Discovery, at AWS re:Invent 2024, alongside its partner NVIDIA. SoftServe’s new solution, built on NVIDIA BioNeMo Blueprints, which are reference processes that expedite AI development and deployment, will streamline drug development, bringing life-changing therapies to market faster and more affordably. The SoftServe Gen AI Drug development system enables researchers, application developers, pharmaceutical corporations, and drug development teams to produce unique drug candidates via molecular screening.
• In October 2024, Evogene Ltd., a leading computational biology company aiming to revolutionize life-science-based product discovery and development, has announced a collaboration with Google Cloud to create a cutting-edge foundation model for generative small molecule de novo design, propelling Evogene’s ChemPass AI tech-engine to new heights of innovation. ChemPass AI is a computational technology platform that guides and expedites the discovery and development of new products based on small molecules. This ground-breaking effort aims to accelerate the discovery and development of novel small molecules for medicine development, sustainable crop protection, and other creative products in a variety of life science industries.

Key Advantages for Stakeholders:

Navistrat Analytics’ industry report provides an in-depth quantitative analysis of various market segments, historical and current trends, market forecasts, and dynamics within the global market. The historical years covered in this report are 2022 to 2023, with 2024 serving as the base year for market size calculations. The forecast period extends from 2025 to 2032.

The report includes an executive summary and a comprehensive overview of market drivers, restraints, opportunities, and challenges (DROC), along with insights into regulatory standards. It features detailed analyses such as PORTER’s Five Forces, SWOT, and PESTLE, as well as assessments of technological trends and the competitive landscape.

PORTER’s Five Forces analysis helps stakeholders evaluate the impact of new entrants, competitive rivalry, supplier power, buyer power, and substitution threats, enabling them to assess the level of competition and the attractiveness of the global market. The competitive landscape provides stakeholders with a clear understanding of the current market positions of key players, offering valuable insights into their competitive environment.

Scope And Key Highlights Of The Global Generative AI in Drug Discovery Market Report:

The Global Generative AI in Drug Discovery Market report offers a detailed analysis of market size, including historical revenue (in USD Million) data for 2022-2023 and revenue forecasts for 2025-2032 across the following segments:

• Technology Outlook (Revenue, USD Million; 2022-2032)
  • Deep learning
  • Machine learning
  • Molecular docking
  • Quantum computing
  • Reinforcement learning
• Application Outlook (Revenue, USD Million; 2022-2032)
  • Molecule design
  • Antibody design & development
  • De novo drug design
  • Precision drug discovery
  • Toxicity Prediction
  • Others
• End-Use Outlook (Revenue, USD Million; 2022-2032)
  • Pharmaceutical & Biotechnology Companies
  • Contract Research Organizations (CROs)
  • Academic And Research Institutions
• Regional Outlook (Revenue, USD Million; 2022-2032)
  • North America
    • U.S.
    • Canada
    • Mexico
  • Europe
    • Germany
    • France
    • U.K.
    • Italy
    • Spain
    • Benelux
    • Nordic Countries
    • Rest of Europe
  • Asia Pacific
    • China
    • India
    • Japan
    • South Korea
    • Oceania
    • ASEAN Countries
    • Rest of APAC
  • Latin America
    • Brazil
    • Rest of LATAM
  • Middle East & Africa
    • GCC Countries
    • South Africa
    • Israel
    • Turkey
    • Rest of MEA

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