How Is the Demand for Faster, Cost-Effective Drug Development Driving AI in Drug Discovery?

The global AI in drug discovery market size reached USD 1.8 Billion in 2024 and is expected to reach USD 14.0 Billion by 2033, exhibiting a CAGR of 23.17% during 2025-2033. This growth is driven by the exponential increase in biological data, AI's predictive capabilities, and the rising demand for cost-effective and faster drug development processes in pharmaceuticals, biotechnology, and research sectors. AI enables enhanced identification of drug candidates, reducing both time and cost of traditional drug development.

STUDY ASSUMPTION YEARS
• Base Year: 2024
• Historical Year/Period: 2019-2024
• Forecast Year/Period: 2025-2033

AI IN DRUG DISCOVERY MARKET KEY TAKEAWAYS

• Current Market Size: USD 1.8 Billion in 2024
• CAGR: 23.17% during 2025-2033
• Forecast Period: 2025-2033

• The demand for drug development processes that are faster and more cost-effective is the main factor that drives the market. These processes notably speed up drug target identification and clinical trial optimization.

• Services hold the majority of the market share since AI-driven research services are a means to cost reduction, efficiency improvement, and by providing access to specialized expertise.

• Optimization of drugs and repurposing accounts for the largest part of the market as AI helps to develop the existing drug molecules.

• Oncology leads the therapeutic area segment, as a result of increasing cancer prevalence and the rising need for personalized therapies.

• North America is at the forefront of the market which is supported by a robust technological infrastructure, the presence of major industry players, and high R&D expenditure.

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MARKET GROWTH FACTORS

One of the main factors attributing to the growth of the AI in drug discovery market is the rapid accumulation of biological and chemical data resulting from progress in biotechnology and genomics. The surge involves intricate data such as molecular structures, protein interactions, and genetic mutations thus requiring advanced computational tools such as AI. AI is the only technology capable of handling and interpreting complex datasets with such a high degree of accuracy, which in turn facilitates the identification of therapeutic candidates is faster and more accurate, thus the drug discovery process is less time-consuming.

In particular, machine learning and deep learning models, which are subsets of AI, have been found to have superior predictive abilities in drug discovery. These models analyze the entire historical data of the drug development process, comprising both successes and failures, to identify the most likely candidates for drug efficacy and side effects. These algorithms assist in the trial-and-error method of drug discovery thus saving on the expenditure of trials and improving success rates and efficiency of drug development.

The conventional drug discovery processes are long and expensive, and they also suffer from high failure risks. AI is revamping this field by a large margin in that it can more than halve the time taken for this development stage and also slash the required funds. By means of virtual screening, compound optimization, and toxicity prediction, AI enables scientists to select the most promising compounds for the experimental work, thus cutting down the unavoidable lab testing. This is a classic case of a paradigm shift where pharmaceutical resources are optimized thus the process of getting new drugs into the market is sped up.

MARKET SEGMENTATION

Breakup by Offering:
• Software
• Services

The market for services is the largest due to the pharmaceutical industry increasingly recognizing AI as a problem solver for challenges such as novel drug target identification and compound optimization.

Breakup by Application:

• Preclinical Testing
• Drug Optimization and Repurposing
• Target Identification
• Candidate Screening
• Others

Drug optimization and repurposing are the major players in the market, driven by AI, which can enhance an old drug, reduce the time of development, and quickly initiate the repurposing of the drug.

Breakup by Therapeutic Area:
• Oncology
• Neurodegenerative Diseases
• Cardiovascular Diseases
• Metabolic Diseases
• Others

The most significant factor behind Oncology's dominance is the rising cancer prevalence coupled with the ever-increasing demand for AI-based identification of therapies and personalized treatments.

Breakup by End User:

• Pharmaceutical and Biotechnology Companies
• Contract Research Organizations (CROs)
• Research Centers and Academic Institutes

Pharmaceutical and biotechnology companies are majorly responsible for the largest share since they are the ones investing heavily in AI to simplify pipelines and keep up with the competition.

Breakup by Region:

North America
• United States
• Canada

Asia-Pacific
• China
• Japan
• India
• South Korea
• Australia
• Indonesia
• Others

Europe

• Germany
• France
• United Kingdom
• Italy
• Spain
• Others

Latin America
• Brazil
• Mexico
• Others

• Middle East and Africa

REGIONAL INSIGHTS

North America AI in drug discovery market is a clear leader, with the largest market share being attributed to the region. The lead is attributed to the region's robust pharmaceutical and biotechnology industry, investments in AI research, and technologically advanced infrastructure. Europe is well-positioned on a backbone of strong academic and research collaborations, and Asia Pacific is expanding at a rapid pace with more biotech companies and government initiatives targeted at AI adoption in drug discovery.

KEY PLAYERS
• Aria Pharmaceuticals Inc.
• Atomwise Inc.
• Benevolent AI
• BioSymetrics Inc
• BPGbio Inc
• Deep Genomics
• Envisagenics
• Euretos
• Evaxion Biotech A/S
• Exscientia
• Insilico Medicin
• NVIDIA Corporation
• Okwin Inc
• XtalPi Inc

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This release was published on openPR.