The global artificial intelligence (AI) in drug discovery market was evaluated at US$ 1.70 billion in 2023 and is expected to attain around US$ 11.93 billion by 2033, growing at a CAGR of 21.5% from 2024 to 2033. The pharmaceutical and healthcare industries are increasingly adopting AI solutions to mitigate the significant financial burdens and potential setbacks associated with traditional virtual screening (VS) methods.
AI In Drug Discovery Market Overview
The rapid growth of the artificial intelligence (AI) in drug discovery market is driven by its transformative impact across various stages of the drug discovery pipeline, including computational approaches like de novo design and property prediction. Contemporary AI methods such as graph neural networks, reinforcement learning, and generative models, alongside structure-based methods like molecular dynamics simulations and molecular docking, significantly enhance drug discovery applications and the analysis of drug responses.
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Open-source databases and AI-based software tools facilitate drug design, challenges in molecule representation, data collection, complexity, labeling, and label disparities. The digitization of medical records, clinical trials, precision medicine, drug discovery, and health policy stands to benefit immensely from these data-driven methods. Novel analytical methods and computational advances have radically transformed drug discovery, with recent progress sparking significant interest in AI applications to improve de novo molecular design, optimization, structure-based drug design, and both pre-clinical and clinical development.
AI has revolutionized drug discovery and development, enabling researchers to predict the bioactivity of drugs more efficiently. By leveraging advanced methodologies such as quantitative structure-activity relationship (QSAR) modeling and molecular docking, AI enhances the accuracy and speed of bioactivity predictions for various compounds. This technological advancement drives substantial growth in the artificial intelligence (AI) in drug discovery market, offering a more cost-effective and reliable approach to drug development.
- In December 2023, Merck launched the first-ever AI solution to integrate drug discovery and synthesis.
- In December 2023, MilliporeSigma also launched the first-ever AI solution to integrate drug discovery and synthesis.
Report Highlights
- North America region has accounted market share of around 56.18% in 2023.
- The APAC market is expected to grow at a CAGR of 21.1% from 2024 to 2033.
- By therapeutic area, the oncology segment has accounted market share of 21% in 2023.
- Based on application, the drug optimization and repurposing segment has captured market share of 51% in 2023.
- The U.S. AI in drug discovery market size was valued at USD 670 million in 2023.
- The U.S. AI in drug discovery market is expected to reach around USD 4,950 million by 2033 with a CAGR of 22.1% from 2024 to 2033.
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Regional Stance
North America, particularly Canada, is poised to represent the largest share of the AI in drug discovery market. The integration of AI in the drug research and development process ensures that Canada remains agile in meeting its population's health needs and preparing for future pandemics and emergencies. The Government of Canada is firmly committed to adopting cutting-edge technologies to accelerate drug discovery and develop effective treatments for Canadians.
Canada's current healthcare landscape is highly conducive to AI adoption, as highlighted by the Canadian Association of Radiologists (CAR), which integrated nature of the Canadian healthcare system as ideal for pooling anonymized medical data from various institutions and provinces to enhance and validate AI tools for patient management. According to the Government of Canada Report on Integrating Robotics, Artificial Intelligence, and 3D Printing Technologies into Canada's Healthcare Systems, AI applications span direct patient care improving diagnostics, prognosis, treatment selection, and enabling robotic surgeries and examinations indirect patient care through optimized hospital workflows and improved inventory management, and homecare with wearable devices and sensors predicting patient needs.
The Asia-Pacific region is anticipated to experience the fastest growth in the AI in drug discovery market, with India emerging as a key player. The rapid expansion of AI in healthcare in India, projected to become a significant market in the coming years, is driven by its ability to create personalized treatment plans, enable remote consultations, and expedite drug discovery. Generative AI (Gen AI) plays a crucial role in data analysis, predicting side effects, and repurposing drugs. In clinical trials, it enhances efficiency by predicting roadblocks.
India's biotech startups are at the forefront of this AI revolution, leveraging Gen AI for patient-centric applications and drug development, contributing to highly targeted therapies and positioning India on the global clinical trial map. Companies are developing AI co-pilots to boost efficiency and productivity across the value chain. To sustain this growth, there is a critical need to focus on building a skilled workforce capable of adapting to these innovative technologies.
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Report Highlights
By Type
The preclinical and clinical testing segment holds the largest market share by type in the AI-driven drug discovery sector. The steady growth of AI applications in pre-clinical drug discovery within the pharmaceutical industry underscores its integral role across the drug discovery value chain. AI technologies enhance every phase from target identification to clinical development. AI is extensively utilized across various medical sectors, including clinical trials, drug discovery and development, understanding target-disease associations, disease prediction, imaging, and precision medicine. This broad application spectrum demonstrates AI's transformative potential in improving efficiency and outcomes throughout the drug discovery and development process.
By Application
The AI in drug discovery market is segmented into five key applications: neurodegenerative disorders, immuno-oncology, cardiovascular diseases, metabolic diseases, and others. Due to the increasing demand for effective cancer treatments, the immuno-oncology segment holds the largest market share. Machine learning (ML) and deep learning (DL) techniques are extensively used to analyze large genomic and proteomic datasets in immuno-oncology. This systematic approach underscores AI's potential in identifying new biomarkers, which can personalize immunotherapy, thereby enhancing treatment efficacy and patient outcomes.
The AI in drug discovery market is segmented into five key applications: neurodegenerative disorders, immuno-oncology, cardiovascular diseases, metabolic diseases, and others. The fastest-growing application area within this market is neurodegenerative disorders. Recent advances in artificial intelligence (AI) have demonstrated significant potential in the diagnosis, prediction, treatment planning, and monitoring of these conditions. AI algorithms can analyze vast amounts of data from diverse sources, including medical images, quantifiable proteins in urine, blood, and cerebrospinal fluid (CSF), genetic information, clinical records, electroencephalography (EEG) signals, and driving behaviors. Alzheimer's disease (AD), one of the most common neurodegenerative disorders, progressively impairs cognitive abilities and memory. AI's capability to integrate and interpret complex datasets enhances our ability to understand, diagnose, and treat neurodegenerative diseases effectively.
By End User
Drug and biotechnology firms hold the largest share of the AI in drug discovery market. Numerous biotech and pharmaceutical companies, along with their investors, are increasingly investing in AI technologies, betting that the latest generation of AI can unlock novel treatments for a wide range of conditions, from COVID-19 to cancer and chronic diseases. By leveraging AI, these companies can accelerate the pre-clinical phase of drug discovery, significantly reducing costs and enhancing efficiency in the development process. This strategic adoption of AI enables firms to innovate rapidly and bring new, effective treatments to market more quickly.
Research institutions, scholarly bodies, and government foundations are expected to experience the fastest growth throughout the projection period in the AI in drug discovery market. These entities are investing heavily in foundational and use-inspired research in AI, data science, and human-computer interaction. Key areas of focus include human language technologies, computer vision, human-AI interaction, and machine learning theory. Artificial intelligence (AI) has proven to be a transformative tool with vast potential across various industries, offering significant benefits to governments, societies, and economies alike. The strategic emphasis on research and development in these fields underscores the critical role of advancing AI technologies to address complex challenges and drive innovation on a broader scale.
Market Dynamics
Driver
AI Enhances Drug Discovery Through Advanced Molecular Insights and Clinical Trial Optimization
Artificial intelligence (AI) plays a pivotal role in advancing the drug discovery market by enhancing understanding of molecular mechanisms, forecasting dose-response relationships in pharmacokinetic/pharmacodynamic modeling, and simplifying toxicology assessments with tools like the Deeptox Algorithm for accurate toxicity predictions. In clinical trials, AI tools significantly improve key processes such as disease identification, gene target pinpointing, and prediction of molecular effects. Furthermore, AI-driven applications streamline medication adherence and enable risk-based monitoring, thereby boosting efficiency and success rates in clinical trials. These capabilities underscore AI's critical contribution to accelerating drug discovery processes and driving market growth in the pharmaceutical sector.
Restraint
Data Challenges
Artificial intelligence (AI) encounters significant data challenges that hinder its potential in the drug discovery market. These challenges include the vast scale, rapid growth, diversity, and inherent uncertainty of pharmaceutical data sets, which can encompass millions of compounds. Traditional machine learning (ML) tools may struggle to handle such large and complex data sets effectively. QSAR-based models, reliant on small training sets and prone to experimental data errors, further exacerbate limitations in AI applications for drug development. The lack of robust experimental validations adds another layer of constraint, impeding the scalability and efficacy of AI-driven approaches in advancing drug discovery efforts. Addressing these data-related obstacles is crucial to unlocking AI's full potential and fostering growth in the pharmaceutical industry.
Opportunity
AI's Role in Drug Formulation and Manufacturing
The incorporation of artificial intelligence (AI) in drug formulation and manufacturing presents significant opportunities for the pharmaceutical industry. AI replaces traditional trial-and-error approaches with computational tools that address formulation design challenges such as stability, dissolution, and porosity using Quantitative Structure-Property Relationship (QSPR) models. Decision-support tools employ rule-based systems to select excipients based on drug physicochemical properties, continuously optimizing processes through feedback mechanisms. Modern manufacturing systems integrate AI to automate operations like Computational Fluid Dynamics (CFD), which analyzes agitation and stress levels in equipment like stirred tanks. This automation enhances efficiency and product quality, aligning with industry demands for advanced manufacturing practices. AI's pivotal role in pharmaceutical operations underscores its potential to drive innovation and growth in the drug discovery market.
In October 2023, Fujitsu and RIKEN developed AI drug discovery technology utilizing generative AI to predict structural changes in proteins.
Recent Developments
- In January 2024, Deloitte expanded its Quartz AI Suite with Atlas AI for Drug Discovery.
- In May 2024, Sanofi, Formation Bio, and OpenAI announced a first-in-class AI collaboration.
- In March 2024, Elsevier and Iktos partnered to deliver an AI-driven synthetic chemistry platform for drug discovery.
- In May 2024, Oregon Therapeutics and Lantern Pharma launched a strategic AI collaboration to optimize the development of the first-in-class drug candidate XCE853, a potent inhibitor of cancer metabolism.
Key Players in the AI In Drug Discovery Market
- NVIDIA CORPORATION
- Microsoft Corporation
- INSILICO MEDICINE INC.
- Schrödinger
- EXSCIENTIA
- Cloud Pharmaceuticals
- CLOUD PHARMACEUTICAL
- TOMWISE, INC
Market Segmentation
By Type
- Preclinical and Clinical Testing
- Molecule Screening
- Target Identification
- De Novo Drug Design and Drug Optimization
By Application
- Neurology
- Infectious Disease
- Oncology
- Others
By Drug Type
- Small Molecules
- Large Molecules
By Offering
- Software
- Services
By Technology
- Machine Learning
- Deep Learning
- Supervised Learning
- Reinforcement Learning
- Unsupervised Learning
- Other Machine Learning Technologies
- Other Technologies
By End User
- Pharmaceutical & Biotechnology Companies
- Contract Research Organizations
- Academics & Research
- Others
By Geography
- North America
- Europe
- Asia Pacific
- Latin America
- Middle East & Africa (MEA)
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