The AI in drug discovery market size is expected to reach USD 35.42 billion by 2034, exhibiting a CAGR of 29.6% during 2025–2034.
The AI in Drug Discovery Market is revolutionizing the pharmaceutical industry by enhancing the process of identifying and developing new drugs through artificial intelligence (AI) technologies. AI is used to analyze large datasets, model biological processes, predict molecular behavior, and identify potential drug candidates, significantly accelerating the drug discovery process and improving the efficiency of research and development. With AI, pharmaceutical companies are able to streamline drug design, reduce costs, and minimize time-to-market for new drugs.
Market Growth Drivers
- Rising Demand for Drug Development Efficiency
- The lengthy and expensive process of traditional drug discovery has created a demand for more efficient and cost-effective solutions. AI enables pharmaceutical companies to speed up the process, from early-stage research to clinical trials, making it a compelling solution.
- Advancements in Machine Learning and Deep Learning
- Machine learning algorithms, especially deep learning and reinforcement learning, have significantly advanced, allowing AI systems to analyze vast amounts of biological, chemical, and clinical data to identify patterns and predict how molecules interact with biological targets.
- Increasing Availability of Data
- The growing availability of large-scale biological, chemical, and clinical data has accelerated the application of AI in drug discovery. AI systems leverage this data to develop predictive models, identify new drug candidates, and optimize drug design.
List of Key Companies in AI in Drug Discovery Market
- Atomwise Inc.
- BenevolentAI
- Berg Health (In January 2023, Berg Health acquired by BPGbio Inc.)
- BioSymetrics, Inc.
- CYCLICA (Acquired by Recursion Pharmaceuticals)
- Exscientia
- GNS Healthcare (In January 2023, the company Rebranded as Aitia)
- Google (DeepMind)
- IBM
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Key Trends in the AI in Drug Discovery Market
- AI for Target Identification and Validation
- AI is increasingly being used to predict new biological targets for drug development. By analyzing genetic, proteomic, and clinical data, AI helps identify novel therapeutic targets, improving the efficiency of the drug discovery process.
- Drug Repurposing with AI
- AI is helping to repurpose existing drugs for new indications by analyzing vast datasets of drug interactions, clinical trials, and real-world data. This is particularly useful in identifying treatments for rare or underserved diseases.
- AI-Driven Drug Design
- AI algorithms, such as generative models and deep learning, are being used to design novel drug candidates by predicting molecular structures, improving their efficacy, and minimizing side effects. This helps in optimizing the drug discovery process at a faster pace.
𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
The research study includes segmental analysis that divides the market into distinct groups or segments based on common characteristics. With market segmentation, businesses can identify specific customer groups that are more likely to be interested in specific products or services. Also, it enables these businesses to focus their marketing efforts and resources more efficiently, leading to higher conversion rates and improved return on investment. Furthermore, segmentation analysis helps companies develop personalized products or services, which can result in increased customer loyalty and improved customer satisfaction.
By Offering Outlook
- Software
- Services
By Technology Outlook
- Machine Learning
- Deep Learning
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
- Other Machine Learning Technologies
- Other Technologies
By Therapeutic Area Outlook
- Oncology
- Neurodegenerative Diseases
- Cardiovascular Disease
- Metabolic Diseases
- Infectious Disease
- Others
By Application Outlook
- Drug optimization & repurposing
- Preclinical testing
- Others
By End User Outlook
- Pharmaceutical & Biotechnology Companies
- Contract Research Organizations
- Research Centers
- Academic & Government Institutes
AI in Drug Discovery Industry Developments
In July 2024, Exscientia plc collaborated with Amazon Web Services (AWS) to leverage the cloud provider’s artificial intelligence (AI) and machine learning (ML) services to strengthen its platform for comprehensive drug discovery and automation.
In May 2024, Google launched AlphaFold 3, a new AI model developed by Google DeepMind and Isomorphic Labs. The company claims that the model accurately predicts the structure of RNA, DNA, proteins, and ligands and how they interact with the potential to transform the understanding of the biological world and drug discovery.
In December 2023, MilliporeSigma launched AIDDISON, the first software-as-a-service platform bridging virtual molecule design with real-world manufacturability via Synthia retrosynthesis software API integration.
The AI in drug discovery market is experiencing rapid growth, driven by advancements in AI technologies, a rising demand for more efficient drug development, and the increasing availability of large-scale biological and chemical data. As AI continues to evolve, it holds the potential to transform the pharmaceutical industry by reducing costs, speeding up the drug discovery process, and enabling more targeted and personalized treatments. With growing investments in AI research, strategic partnerships, and increasing applications across various stages of drug development, the market is set to see substantial growth and innovation in the coming years.