The AI in Drug Discovery Market is expected to reach a valuation of US$ 8.10 billion by 2030, growing at a CAGR of 25% during the forecast period from 2023 to 2030.
In recent times, Artificial Intelligence (AI) has emerged as a powerful force that is revolutionizing various sectors of society, with the pharmaceutical industry at the forefront of benefiting from its advancements. The integration of AI into drug discovery and development has propelled the growth of the pharmaceutical sector, ushering in a new era of innovation and transformation.
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This surge is evident through the emergence of numerous start-ups dedicated to AI-driven drug discovery, a growing number of partnerships focused on this area, and unprecedented levels of investment pouring into the field. This heightened interest in AI’s role in drug discovery is a testament to its potential to reshape the pharmaceutical landscape.
By leveraging AI, scientists and researchers can expedite the process of identifying potential drug candidates, predicting their efficacy, and optimizing their development. This technology enables the analysis of vast amounts of data, including patient information, genetic data, and molecular structures, to uncover patterns and insights that were previously inaccessible. As a result, drug discovery becomes more efficient, precise, and cost-effective.
AI Reducing Overall Cost and Duration of Drug Development: A Major Factor Increasing the Market Revenue
The market is currently experiencing rapid growth due to the increasing demand for cost and time-efficient drug development. The process of discovering and developing a drug traditionally takes over ten years and costs an average of US$2.8 billion. However, despite these extensive efforts, only one out of ten therapeutic molecules successfully pass Phase II clinical trials and obtain regulatory approval.
In order to address these challenges, the industry has turned to algorithms such as Nearest-Neighbour classifiers, RF, extreme learning machines, SVMs, and deep neural networks (DNNs). These algorithms are utilized in Virtual Screening (VS) to determine synthesis feasibility and predict in vivo activity and toxicity. By leveraging these advanced technologies, pharmaceutical companies like Bayer, Roche, and Pfizer have joined forces with IT companies to establish platforms dedicated to the discovery of therapies in fields like immuno-oncology and cardiovascular disease. Through these collaborations, they aim to accelerate the development of innovative treatments and revolutionize the healthcare industry.
Oncology Therapeutic Area Majorly Adopt AI in Drug Discovery
The oncology therapeutic area has indeed seen significant adoption of AI in drug discovery. This is due to several factors, including the economic potential of oncology drugs, the need for personalized treatment options, and the complexity of cancer biology.
The need for personalized treatment options in oncology has fueled the adoption of AI. Cancer is a complex disease with diverse genetic and molecular characteristics, making personalized treatment crucial for improved patient outcomes. AI algorithms can analyze vast amounts of patient data, including genetic profiles and electronic medical records, to identify disease markers and develop individualized treatment strategies.
The intricate nature of cancer biology poses challenges in understanding the disease and identifying effective treatments. AI algorithms can analyze complex datasets, such as genomic data and molecular interactions, to uncover patterns and relationships that may not be readily apparent to human researchers. This enables the discovery of novel therapeutic targets and the optimization of drug candidates.
North America Emerges as a Frontrunner in the Global AI in Drug Discovery Market
North America has emerged as a leader in AI adoption, particularly in the realm of drug discovery. The region’s strong investment in research and development, coupled with its prominent pharmaceutical industry and partnerships with esteemed institutions, has paved the way for advancements that can revolutionize the way diseases are understood and treated.
The United States has a significant incidence of metabolic and lifestyle diseases. According to the CDC, over 130 million adults in the country will have diabetes in 2022. The National Institutes of Health reports that one in seven adults in the United States is affected by chronic kidney disease. Various research and academic institutions, such as the University of Texas MD Anderson Cancer Center, the University of Alabama in Huntsville, the University of Oxford, and the University of Dundee, are incorporating AI into drug discovery studies. This is due to the country’s large patient population, high prevalence of chronic and infectious diseases, advanced healthcare infrastructure, and active clinical research and trials in the field of AI and drug discovery.
North America has made significant investments in research and development, particularly in the field of AI. This commitment has enabled the region to foster innovation and stay at the forefront of AI advancements. The availability of substantial funding and resources has facilitated the development of advanced AI technologies for drug discovery.
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The United States, in particular, is home to several tech giants that have made significant contributions to the field of AI. Companies such as Google, Microsoft, IBM, and others have developed cutting-edge AI technologies and have established corporate offices in the US. Their expertise and resources have played a pivotal role in driving AI adoption in drug discovery.
Key Players Profiled in the Global AI in Drug Discovery Market
- Envisagenics
- IBM Corporation
- Bioage
- Microsoft Corporation
- Exscientia
- Insilico Medicine
- NVIDIA Corporation
- Atomwise, Inc.
- Cloud Pharmaceuticals, Inc.
- Deep Genomics
- Indivumed GmbH
- Other Prominent Players
Segmentation Overview
By Offering
- Software
- Service
By Technology
- Machine Learning
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- Deep Learning
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- Supervised Learning
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- Reinforcement Learning
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- Unsupervised Learning
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- Other Machine Learning Technologies
- Others Technologies
By Application
- Drug optimization and repurposing
- Preclinical testing
- Others
By Therapeutic Area
- Cardiovascular Disease
- Infectious Disease
- Metabolic Diseases
- Neurodegenerative Diseases
- Oncology
- Others
By End-User
- Contract Research Organizations
- Pharmaceutical & Biotechnology Companies
- Research Centres and Academic and government Institutes
By Region
- North America
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- The U.S.
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- Canada
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- Mexico
- Europe
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- Western Europe
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- The UK
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- Germany
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- France
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- Italy
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- Spain
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- Rest of Western Europe
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- Eastern Europe
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- Poland
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- Russia
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- Rest of Eastern Europe
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- Asia Pacific
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- China
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- India
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- Japan
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- Australia & New Zealand
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- South Korea
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- ASEAN
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- Rest of Asia Pacific
- Middle East & Africa
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- UAE
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- Saudi Arabia
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- South Africa
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- Rest of MEA
- South America
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- Argentina
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- Brazil
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- Rest of South America
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