Dublin, Oct. 14, 2024 (GLOBE NEWSWIRE) -- The "Artificial Intelligence (AI) In Drug Discovery - Market Insight, Competitive Landscape And Market Forecast - 2030" report has been added to ResearchAndMarkets.com's offering.
Artificial Intelligence (AI) in drug discovery market is estimated to advance at a CAGR of 37.67% during the forecast period from 2024 to 2030
The AI in drug discovery market is witnessing positive growth owing to factors such as the rising prevalence of various diseases across the globe, advantages of AI in the pharmaceutical sector, and investments in drug research and development, among others. In addition, extensive partnerships and collaborations between public and private entities at the both national and international levels are further expected to boost the AI in drug discovery market during the forecast period from 2024 to 2030.
AI in Drug Discovery Market Dynamics:
One of the key aspects influencing AI growth in the drug discovery market is a high capital investment in the drug discovery and development process. Following the conventional method of drug discovery and drug development results in the consumption of 12-14 years till a final product, the authorized drug reaches the market for end-use. For example, as per the Pharmaceutical Research and Manufacturers of America, on average, a drug takes about 10 years to get to the market with clinical trials accounting for 6-7 years out of the total period. The same source further stated that the average cost to develop each successful drug comes out to be approximately USD 2.5 billion.
Another aspect of including AI in drug discovery and drug development process is leveraging the technology to understand the patterns in the already published data to identify trending areas of research for different diseases that may provide insights regarding any scientific progress that may be utilized in initiating a new drug development program. This is done by using natural language processing (NLP) that helps in data mining and creating interconnected knowledge graphs. These knowledge graphs are essentially a threading together of the data from different areas of drug development such as disease-related data, drug related data, or chemical/biological entity related data.
Furthermore, the adoption of AI solutions in the clinical trial process eliminates possible obstacles, helps in the reduction of clinical trial cycle time, and significantly improves the productivity and accuracy of the clinical trial process. Therefore, the adoption of these advanced AI solutions in drug discovery processes is gaining popularity amongst life science industry stakeholders.
AI in Drug Discovery Market Segment Analysis:
One of the cutting-edge AI methodologies employed in de novo drug design is deep reinforcement learning (DRL). DRL integrates artificial neural networks with reinforcement learning architectures, enabling the system to learn and adapt through trial and error. A notable example of DRL in de novo drug design is the use of recurrent neural networks (RNNs). RNNs are particularly well-suited for analyzing sequential data, such as text or molecules represented as a sequence of characters like SMILES (Simplified Molecular Input Line Entry System).
RNNs operate by processing input data sequentially, one step at a time, allowing them to recognize and learn patterns within SMILES strings. This capability is crucial for generating chemically plausible molecules in the de novo design process. The molecules produced through this method are driven by chemical principles, ensuring their potential viability as drug candidates. The success of RNNs in de novo drug design underscores the transformative potential of AI in this field.
Considering these advantages, AI-driven de novo drug design is poised to become a key application area in drug discovery in the coming years. The ability of AI to expedite the drug development process and generate innovative molecular targets is expected to contribute significantly to market growth. As the pharmaceutical industry continues to embrace AI technologies, the impact on drug discovery and development will likely be profound, driving advancements and opening new avenues for therapeutic innovation during the forecast period from 2024 to 2030.
North America is expected to dominate the overall AI in drug discovery market:
North America is estimated to amass the largest revenue share in the AI in drug discovery market in the year 2023. This can be ascribed to the presence of a large patient pool associated with various diseases including cancers, and neurological disorders which in turn drive the demand for various drugs with minimal side effects. Moreover, the extensive focus on clinical research and the presence of key players in the region from both the pharmaceutical as well as technology domains further help in the growth of North America AI in drug discovery market.
One of the key supporting factors for the growth of North America region in the AI in drug discovery market is the increasing prevalence of various diseases across the region. For example, one of the prominent reasons for the requirement for high number of drugs is the surge in the prevalence of cancers in the US. National Cancer Institute 2024 estimated that 2 million new cases of cancer will be diagnosed in the US by the end of 2024. Furthermore, it is estimated that prostate, lung, and colorectal cancers are to represent approximately 48% of all cancer diagnoses in men. For women, the most prevalent cancers are breast, lung, and colorectal, which are expected to account for about 51% of all new cancer diagnoses.
AI in Drug Discovery Market key players:
Some of the key market players operating in the AI in Drug Discovery Market include IBM Corporation, Numedii Inc, Deep Genomics, NVIDIA Corporation, Atomwise Inc, Cloud Pharmaceuticals Inc, Alphabet Inc (DeepMind), Insilico Medicine, BenevolentAI, Exscientia, Cyclia, Valo Health, Owkin Inc, Verge Genomics, BioSymetrics, and others.
Recent Developmental Activities in the AI in Drug Discovery Market:
- In May 2024, Sanofi, Formation Bio, and Open AI entered into a collaboration to develop AI-powered software aimed at accelerating drug development and enhancing the efficiency of bringing new medicines to patients. Together, they aimed to create customized, purpose-built solutions designed to optimize various stages of the drug development lifecycle, thereby streamlining processes and improving outcomes in pharmaceutical innovation.
Key Topics Covered:
1. AI in Drug Discovery Market Report Introduction
1.1. Scope of the Study
1.2. Market Segmentation
1.3. Market Assumption
2. AI in Drug Discovery Market Executive Summary
2.1. Market at Glance
3. Competitive Landscape
4. Regulatory Analysis
4.1. The United States
4.2. Europe
4.3. Japan
4.4. China
5. AI in Drug Discovery Market Key Factors Analysis
5.1. AI in Drug Discovery Market Drivers
5.1.1. Increasing prevalence of chronic diseases
5.1.2. Growing interest in leveraging artificial intelligence in drug development owing to its advantages
5.1.3. Growing collaboration between public and private entities operating in pharma and AI domains
5.2. AI in Drug Discovery Market Restraints and Challenges
5.2.1. Knowledge gaps between biologics/chemists and AI scientists
5.2.2. Limitations of traditional machine learning tools in data handling
5.3. AI in Drug Discovery Market Opportunities
5.3.1. Development of safer cancer therapies with minimal side-effects
6. AI in Drug Discovery Market Porter's Five Forces Analysis
6.1. Bargaining Power of Suppliers
6.2. Bargaining Power of Consumers
6.3. Threat of New Entrants
6.4. Threat of Substitutes
6.5. Competitive Rivalry
7. AI in Drug Discovery Market Assessment
7.1. By Type
7.1.1. De Novo Drug Design and Optimization
7.1.2. Preclinical Development
7.1.3. Others
7.2. By Application
7.2.1. Oncology
7.2.2. Cardiovascular
7.2.3. Infectious Diseases
7.2.4. Others
7.3. By End-User
7.3.1. Pharmaceutical and Biotechnology Companies
7.3.2. Contract Research Organizations (CROs)
7.3.3. Others
7.4. By Geography
8. AI in Drug Discovery Market Company and Product Profiles
8.1. Company Overview
8.2. Company Snapshot
8.3. Financial Overview
8.4. Product Listing
8.5. Entropy
9. KOL Views
10. Project Approach
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