Global AI in Drug Discovery market was valued at US$ 281 million in 2019 and is expected to reach US$ 1597.02 million by 2028, growing at a CAGR of 35.64% over the forecast period. The rising use of artificial intelligence (AI) systems aimed at reducing the overall cost of drug discovery and improving outcomes is expected to boost demand for these platforms in the pharmaceutical industry. Artificial intelligence for drug discovery is a technology that uses and adds value to various algorithms in drug discovery during decision-making processes. In recent years, the increasing need to reduce the cost of new drug development and production is expected to strengthen the market for artificial intelligence (AI) platforms in the field of drug discovery. The estimated novel drug discovery and production cost is USD 2.6 billion, according to industry journals. This is largely due to the small development research funnel that excludes much of the preclinical and phase 1 study candidate therapies. As a result, major players in the pharmaceutical industry has started the adoption of artificial intelligence for a quicker, more reliable, and cost-effective drug development.
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Over the years, the adoption of artificial intelligence in drug development has increased a lot due to the monumental data produced by the processes of molecule screening and preclinical studies. The vast volume of data makes it impossible for the researcher to study all the scientific literature. In such situations, artificial intelligence can help improve the screening process and reduce the time taken for drug molecule interactions and histological data to be analyzed and identified. In addition, most major bio pharmaceutical companies are now pursuing AI-driven drug development solutions and are making a range of deals these include the purchase of asset intellectual property rights, option exercise fees, royalties, fees for access to technology and revenue based on the sub-licensing or selling of drugs produced by the partnership. Most biopharmaceutical companies have implemented a number of strategies over the years to successfully incorporate AI into the process of discovery, such as setting up their own dedicated teams by recruiting AI experts and data analysts, investing in start-ups, and forming partnerships with tech giants and research centers.
The pattern that is becoming consistent is the implementation at various stages of the drug development process of more than one AI solution and the diversification of deals and partnerships. In order to remain competitive and agile in a world of rapidly rising awareness, biopharmaceutical companies are increasingly outsourcing research activities to contract research organizations (CROs). The companies plan to use the power of AI to put patients first and change their lives tangibly by providing a way to reduce the cost of drug discovery and development, reduce the rate of failure and increase the pace at which medicines are delivered to patients. At present, because of increased R&D costs and decreased productivity, the pharmaceutical industry faces difficulties in maintaining its drug development programs.
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Another factor which is projected to improve the AI for the drug discovery market is the increased pressure on the drug maker to lower the drug price. This is because Al decreased the failure rate of clinical trials and also removed the expense of long-term drug discovery research and development. Helix, an AI start-up, uses machine learning to respond to verbal questions and requests, allowing researchers to increase productivity, enhance laboratory safety, stay up to date on important research topics, and manage inventory. Market growth is expected to be fueled by an increase in the value of drug discovery and an increase in the funding of R&D activities for the use of AI technology in the field of drug discovery. One of the fields that accounts for high loss of sales and low returns is preclinical research. Preclinical research optimization can help reduce the cost. This is where algorithm-based artificial intelligence models can be used for accurate study of the response of human physiology. Models based on AI can help reduce experimental costs and help to predict cross-species differences effectively.
The detailed research study provides qualitative and quantitative analysis of AI in Drug Discovery market. The market has been analyzed from demand as well as supply side. The demand side analysis covers market revenue across regions and further across all the major countries. The supply side analysis covers the major market players and their regional and global presence and strategies. The geographical analysis done emphasizes on each of the major countries across North America, Europe, Asia Pacific, Middle East & Africa and Latin America.
Key Findings of the Report:
- In the therapeutic area oncology segment will exhibit significant CAGR over the period. In the early detection of cancer, AI already plays a crucial role. In addition, as cancer treatments for each patient can differ, personalized medicine has proven to be an effective cancer treatment alternative. Oncologists are aided by artificial intelligence platforms designed to classify genetic mutations to design successful personalized patient care.
- Infectious diseases segment is also expected to expand over the period, large amounts of sample data is being needed by the AI platforms to begin identifying key trends and points. In infectious disease diagnostics and treatment design, the adoption of AI systems has begun to develop and is expected to experience robust growth in the coming years.
- North America dominated the market because of the growing adoption of artificial intelligence and the existence of a large number of market players.
- Over the forecast era, Asia Pacific is projected to emerge as the fastest-growing region in the sector. It is anticipated that the increasing adoption of new technologies in India and China for new drug production and an emphasis on improving pharmaceutical capability within countries would drive the market in this region.
- The major players operating in the market are Atomwise, Inc, BenevolentAI Ltd, BERG LLC, BIOAGE, Cloud Pharmaceuticals, Inc., DEEP GENOMICS, Envisagenics, EVAXION BIOTECH A/S, Exscientia, Google LLC, HACARUS INC, IBM Corp, Insilico Medicine, Microsoft, NuMedii, Inc., OWKIN, INC., twoXAR, Incorporated, VERGE genomics, XtalPi Inc., Cyclica and Other Market Participants.
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AI in Drug Discovery Market:
- By Drug Type
- Small Molecule
- Large Molecule
- By Technology
- Deep Learning
- Machine Learning
- Others
- By Application
- Target Identification and Validation
- Drugs Design and Redesign
- Preclinical Testing
- Others
- By Organization Size
- Small and Medium Organizations
- Large Organizations
- By Therapeutic Area
- Cardiovascular Disease
- Metabolic Disease
- Neurodegenerative Diseases
- Dermatological Diseases
- Infectious Diseases
- Oncology
- Others
- By End Users
- Pharmaceutical Companies
- Research Centers and Laboratories
- Clinicians and Drug Developers
- Others
- By Region:
- North America
- U.S.
- Canada
- Mexico
- Rest of North America
- Europe
- France
- The UK
- Spain
- Germany
- Italy
- Nordic Countries
- Denmark
- Finland
- Iceland
- Sweden
- Norway
- Benelux Union
- Belgium
- The Netherlands
- Luxembourg
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- New Zealand
- Australia
- South Korea
- Southeast Asia
- Indonesia
- Thailand
- Malaysia
- Singapore
- Rest of Southeast Asia
- Rest of Asia Pacific
- Middle East & Africa
- Saudi Arabia
- UAE
- Egypt
- Kuwait
- South Africa
- Rest of Middle East & Africa
- Latin America
- Brazil
- Argentina
- Rest of Latin America
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