The global AI in drug discovery market size will hit around USD 15.44 billion by 2032 and growing at a CAGR of 29.66% from 2023 to 2032.
The global AI in drug discovery market size will hit around USD 15.44 billion by 2032 and growing at a CAGR of 29.66% from 2023 to 2032. The AI in the drug discovery market is driven by the growing demand for the discovery and development of novel drug therapies and increasing manufacturing capacities of the life science industry.
Manufacturers in the life science industry constantly focus on replenishing their product pipelines as the majority of the big sellers go off patent. Furthermore, a growing number of public-private partnerships boosting the adoption of AI-powered solutions in drug discovery and development processes is driving the market. Countries such as France, the U.S., Spain, and Japan dominate the clinical trial space, while the U.K. is focused on enhancing research & development activities.
Biotechs are applying AI and machine learning to drug development, potentially creating dozens of new medicines and a $50 billion market over the next decade. Here’s what that means for patients and investors.
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For biotechnology companies, much of the traditional process of discovering new drugs is costly guesswork. But a new wave of drug development platforms, enabled by artificial intelligence, is helping companies use vast data sets to quickly identify patient response markers and develop viable drug targets more cheaply and efficiently.
The results could be transformative not just for medical providers and patients suffering from hard-to-treat diseases, but for the biotech sector: Morgan Stanley Research believes that modest improvements in early-stage drug development success rates enabled by the use of artificial intelligence and machine learning could lead to an additional 50 novel therapies over a 10-year period, which could translate to a more than $50 billion opportunity.
“Predictive diagnostics, enhanced by data, present a significant near-term opportunity for the life sciences industry,” says Tejas Savant, who covers life science tools and diagnostics at Morgan Stanley Research. “It’s also likely to resonate with payors, since these trials can generate better outcomes. They can also deliver sizable cost savings by enabling earlier identification and treatment of higher-risk patients.”
The AI Opportunity
Technological advances in recent years have made it easier to capture and store reams of digital patient data. This has resulted in rich troves of genomic data, health records, medical imaging and other patient information that AI platforms can mine to help to develop drugs faster and with greater chance of success in the early stages of creation.
Morgan Stanley Research biotechnology analysts Matthew Harrison and Vikram Purohit estimate that “a 20% to 40% reduction in costs for preclinical development across a subset of U.S. biotech companies could generate the cost savings needed to fund the successful development of four to eight novel molecules.”
This would represent as much as a 15% increase of approved therapies over the total number of novel drug approvals in 2021, demonstrating the potential for biotechs to generate new revenue while helping more patients.
The pairing of AI and big data could help patients in other ways. In addition to drug discovery and development, advanced data-analysis capabilities and richer data sets could help medical professionals assess patients’ risk and detect disease earlier.
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Key Pointers
- North America region dominated the market with the largest market share of 59% in 2022.
- By Application, the drug optimization and repurposing segment registered the maximum market share of 54% in 2022.
- By Therapeutic Area, the oncology sub-segment captured the maximum market share of 24% in 2022.
- By Therapeutic Area, the infectious diseases segment is predicted to grow at the fastest CAGR from 2023 to 2032.
Creating a Foundation for Valuation
For biotech firms, it can take a blockbuster drug discovery just to break even. The median investment required to bring a new drug to market is estimated to be nearly $1 billion, while the true cost of research and development may be as high as $2.5 billion per marketed therapy, when factoring in abandoned trials and clinical failures.
That means that savings from AI could offer significant value. But with the high risks involved in creating biologically feasible treatments, and the limited history of the technology platforms involved, investors will need to see solid evidence for real-world use cases for AI-enabled drug discovery.
Morgan Stanley Research analysts anticipate an inflection point for the sector, driven by data readouts from drug trials over the next two years. An increase in collaboration between AI drug developers and large biopharma companies could also make a difference.
“If initial readouts are consecutively strong, we believe stocks across the space could rise as investors gain confidence in a well-defined total addressable market for AI-enabled drug development,” says Purohit, who covers small and mid-size biotechs. “In addition to strong data, we expect the market to look for concrete steps forward with biopharma partnerships as proof of validation.”
An AI drug development platform could generate significant revenue growth through partnerships, assuming modest annual increases to AI investment within biopharma research and development budgets.
Along with new data and progress on partnerships, investors will have to weigh how individual firms are using AI and machine learning to develop drugs. They should also consider the biotech industry’s range of business models, with revenue coming from a mix of proprietary pipeline development and a combination of milestone payments and royalties from programs developed with biopharma partners.
Application Insights
The drug optimization and repurposing segment accounted for the largest revenue share of 54% in 2022. In the drug optimization and repurposing, AI algorithms analyze extensive datasets to identify existing drugs that can be repurposed for new therapeutic purposes. By mining biological and chemical databases, AI identifies compounds with the potential to target different diseases, thus saving valuable time and resources. This approach significantly expedites the drug development process, allowing researchers to focus on promising candidates and streamline clinical trials.
In preclinical testing, AI plays a pivotal role in predicting the efficacy and safety of potential drug candidates. By analyzing complex biological data, AI models can simulate drug interactions with target proteins and predict the outcomes of preclinical experiments. This predictive capability enables researchers to prioritize the most viable candidates for further testing, ensuring that only the most promising compounds advance to the next stages of development. AI-driven preclinical testing also enhances the understanding of drug mechanisms, enabling researchers to make data-driven decisions regarding dosage, formulation, and potential side effects.
Therapeutic Area Insights
The oncology sub-segment generated the maximum market share of 24% in 2022. In oncology, AI technologies have revolutionized the approach to cancer research and treatment. By analyzing vast datasets encompassing genomics, proteomics, and clinical information, AI algorithms assist in identifying specific genetic mutations and protein interactions associated with various cancer types. This precise understanding of the molecular basis of cancer enables the development of targeted therapies. It has paved the way for personalized medicine, where treatments are tailored to individual patients based on their genetic profiles. Moreover, AI contributes to the repurposing of existing drugs for novel therapeutic applications, significantly expediting drug discovery processes in the field of oncology.
The infectious diseases segment is expected to grow at the fastest CAGR during the forecast period. The infectious diseases, AI is playing a pivotal role in combating a wide range of pathogens. AI’s ability to analyze and interpret diverse biological data, including pathogen genomes, host responses, and clinical information, enables the rapid identification of potential drug targets within pathogens. By understanding the genetic makeup of viruses and bacteria, researchers can design specific inhibitors and vaccines to combat infectious diseases effectively. AI-driven predictive modeling also helps anticipate how pathogens might evolve and develop resistance, allowing for the development of strategies to counteract these challenges proactively.
Regional Insights
North America held the largest revenue share of over 59% in 2022. In North America, particularly in the United States, extensive research and development initiatives have propelled the region to the forefront of AI-driven drug discovery. The presence of leading pharmaceutical companies, research institutions, and technology firms has fostered a conducive environment for innovation. Moreover, collaborations between academia and industry players have accelerated the integration of AI technologies into drug discovery processes, leading to a robust market growth in the region.
Asia-Pacific, especially countries like China, Japan, and India, has emerged as a key player in the global AI in drug discovery market. Rapid technological advancements, a burgeoning biotechnology sector, and increasing research funding have fueled the adoption of AI-driven approaches. In China, for instance, government initiatives and investments in AI research have led to the establishment of innovative startups and research centers specializing in AI applications for drug discovery. Similarly, Japan and India have witnessed a surge in AI-based drug discovery initiatives, with a focus on addressing prevalent diseases in their respective populations.
Key Players
- IBM Watson
- Exscientia
- GNS Healthcare
- Alphabet (DeepMind)
- Benevolent AI
- BioSymetrics
- Euretos
- Berg Health
- Atomwise
- Insitro
- Cyclica
Artificial Intelligence In Drug Discovery Market Segmentations:
By Application
- Drug Optimization and Repurposing
- Preclinical Testing
- Others
By Therapeutic Area
- Oncology
- Neurodegenerative Diseases
- Cardiovascular Diseases
- Metabolic Diseases
- Infectious Diseases
- Others
By Region
- North America
- Europe
- Asia Pacific
- Latin America
- MEA
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