Artificial Intelligence in Drug Discovery Market: Current Analysis and Forecast (2021-2027)
$3500 – $6860
Emphasis on Technology [Machine Learning (Deep Learning, Supervised Learning, Reinforcement Learning, Unsupervised Learning, Others), Other Technologies]; Components (Software, Services); Application (Drug Optimization & Repurposing, Preclinical Testing, Others); Therapeutic Area (Cardiovascular Disease, Infectious Disease, Metabolic Diseases, Neurodegenerative Diseases, Oncology, Others); Drug Type (Small Molecule, Large Molecule); End-User (Contract Research Organizations, Pharmaceutical & Biotechnology Companies, Research Centers and Academic & Government Institutes); and Region & Country.
Artificial Intelligence in drug discovery holds enormous opportunities for the healthcare industry. The application of AI reduces the R&D gap in the drug production process and assists researchers in the targeted production of drugs. Artificial Intelligence in drug discovery seeking the interest of many investors towards drug development. For instance, according to Deloitte in 2020, China has been a major investor for biotech companies in the United States over the past few years. These investments increased significantly in 2019, with USD 1.4 billion into US-based biotech and drug firms, compared to just USD 125.5 million in 2018. Also, the mounting pressure on drug developing companies to reduce drug prices is another factor that is anticipated to boost the AI in drug discovery market during the forecast period.
Moreover, the numerous options provided by artificial intelligence platforms like data mining, targeted protein structures, and customization capabilities will certainly increase the adoption of AI by the pharmaceutical and biotech industries. These advancements with the help of machine learning and deep learning enable pharmaceutical companies to precisely recognize the molecule binding properties of drugs with high accuracy. For instance, in 2018 GlaxoSmithKline plc, a multinational pharmaceutical company invested USD 300 million in “23 and Me” a gene testing company. This deal helped the company to gain access to databases so that the company will generate specific information regarding the relationship between genes and diseases, which further help the company in the development of novel drugs for rare diseases and is a major factor that is contributing to the growing market of artificial intelligence in drug discovery, globally.
Furthermore, developing drugs to combat Covid-19 is a global priority, requiring communities to come together to fight the spread of infection. For instance, At MIT on 27 April 2020, researchers with backgrounds in machine learning and life sciences were collaborating, sharing datasets and tools to develop machine learning methods to identify novel cures for Covid-19.
Global Funding in Artificial Intelligence in Drug Discovery, 2012-2020 (USD Mn)
IBM Corporation, Microsoft, Google, NVIDIA Corporation, Atomwise, Inc., Insilico Medicine, BIOAGE, BenevolentAI, Numerate, and NuMedii are some of the prominent players operating in the global Artificial Intelligence in Drug Discovery market. Several M&As along with partnerships have been undertaken by these players to facilitate customers with hi-tech and innovative products.
Insights Presented in the Report
“Amongst Technology, Machine learning segment holds the major share”
Based on Technology, the market is bifurcated into Machine Learning and Other technologies. The Machine Learning segment dominated the market in 2020. As the advances in wireless technology, miniaturization, and computational power with the use of machine learning architectures are fueling the development of more refined and powerful AI tools.
“Amongst Component, Software segment is anticipated to grow at the highest CAGR during the analyzed period”
Based on Components, the market is bifurcated into Software and Service. The Software segment accounted for the major revenue portion in 2020. As the Companies which use software has low cost and takes less time to market the drug with low failure rates.
“Amongst Drug Type, Small molecule segment is anticipated to grow at the highest CAGR during the analyzed period”
Based on Drug Type, the market is bifurcated into Small molecules and Large molecules. The Small molecule segment accounted for the major revenue portion in 2020. The segment is anticipated to observe significant growth in the upcoming years as their small size makes them easily ingestible in the gastrointestinal tract where active substances are immediately absorbed into the bloodstream and can travel anywhere in the body.
“Amongst Application, Drug optimization and repurposing segment holds the major share”
Based on Application, the market is bifurcated into Drug optimization and repurposing, Preclinical testing, and others. The Drug optimization and repurposing segment accounted for the major revenue portion in 2020. As the AI platforms help in identifying alternative applications for existing medicines which can help pharma companies expand their collection of offerings and assist in producing alternative therapies through repurposing in pharmaceutical products.
“Amongst Therapeutic Area, Oncology segment is anticipated to grow at the highest CAGR during the analyzed period”
Based on Therapeutic Area, the market is bifurcated into Cardiovascular Diseases, Infectious Diseases, Metabolic Diseases, Neurogenerative Diseases, Oncology, and Others. The Oncology segment accounted for the major revenue portion in 2020. As, as AI plays an important role in the early identification of cancer. Moreover, cancer treatments may be different for every patient and personalized medicine has proven to be an actual alternative.
“Amongst End-user, Pharmaceutical & Biotechnology Companies segment holds the major share”
Based on End-user, the market is bifurcated into Contract Research Organizations, Pharmaceutical & Biotechnology Companies, and Research Centers, and Academic & Government Institutes. The Pharmaceutical & Biotechnology Companies segment is anticipated to observe lucrative growth. As they are more prone to work in integration with bioinformatics, computational engineering, nanotechnology, and pharmacogenomics methods into the drug discovery process which will lead to the next stage of advances in drug discovery.
“North America signifies one of the largest markets of Artificial Intelligence in Drug Discovery Market”
For a better understanding of the market dynamics of the Artificial Intelligence in Drug Discovery market, a detailed analysis was conducted for different regions across the globe including North America (United States, Canada, and the Rest of North America), Europe (Germany, France, Italy, Spain, United Kingdom and Rest of Europe), Asia-Pacific (China, Japan, India, Australia and Rest of APAC), Rest of World. North America constitutes a major market for the Artificial Intelligence in Drug Discovery industry and generated the maximum revenue in 2020 owing to the presence of key companies and healthcare infrastructure with the highest spending’s in the world. However, the European region would also grow at the same pace during the forecast period.
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- The study includes market sizing and forecasting analysis validated by authenticated key industry experts
- The report presents a quick review of overall industry performance at one glance
- The report covers an in-depth analysis of prominent industry peers with a primary focus on key business financials, product portfolio, expansion strategies, and recent developments
- Detailed examination of drivers, restraints, key trends, and opportunities prevailing in the industry
- The study comprehensively covers the market across different segments
- Deep dive regional level analysis of the industry
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|1.2||Objective of the Study|
|1.5||Currency Used in Report|
|1.6||Scope of the Global Artificial Intelligence in Drug Discovery Market Study|
|2.1||Research Methodology for the Global Artificial Intelligence in Drug Discovery Market|
|2.1.1||Main Objective of the Global Artificial Intelligence in Drug Discovery Market|
|8.1.5||Other Machine Learning Technologies|
|11.1||Drug Optimization and Repurposing|
|13.1||Contract Research Organizations|
|13.2||Pharmaceutical & Biotechnology Companies|
|13.3||Research Centers and Academic & Government Institutes|
|14.1||North America Artificial Intelligence in Drug Discovery Market|
|14.1.3||Rest of North America|
|14.2||Europe Artificial Intelligence in Drug Discovery Market|
|14.2.6||Rest of Europe|
|14.3||Asia Pacific Artificial Intelligence in Drug Discovery Market|
|14.3.5||Rest of Asia Pacific|
|14.4||Rest of World Artificial Intelligence in Drug Discovery Market|
|19.1||Demand Side Analysis|
|19.2||Supply Side Analysis|
|19.2.1||Top Product Launches|
|19.2.2||Top Business Partnerships|
|19.2.3||Top Business Expansions, Investments and Divestitures|
|19.2.4||Top Merger and Acquisitions|
|21.1||Porter’s Five Forces Analysis|
|21.1.1||Bargaining power of Supplier|
|21.1.2||Bargaining power of Buyer|
|21.1.4||Availability of Substitute|
|21.1.5||Threat of new Entrants|
|21.2.1||Company Shares, By Revenue|
Analyzing the historical market, estimation of the current market, and forecasting the future market of the Global Artificial Intelligence in Drug Discovery Market were the three major steps undertaken to create and analyze the adoption of artificial intelligence in drug discovery across major regions globally. Exhaustive secondary research was conducted to collect the historical market numbers and estimate the current market size. Secondly, to validate these insights, numerous findings and assumptions were taken into consideration. Moreover, exhaustive primary interviews were also conducted, with industry experts across the value chain of the artificial intelligence in drug discovery sector. Post assumption and validation of market numbers through primary interviews, we employed a top-down/bottom-up approach to forecast the complete market size. Thereafter, market breakdown and data triangulation methods were adopted to estimate and analyze the market size of segments and sub-segments the industry pertains to. Detailed methodology is explained below:
Analysis of Historical Market Size
Step 1: In-Depth Study of Secondary Sources:
Detailed secondary study was conducted to obtain the historical market size of the artificial intelligence in drug discovery through company internal sources such as annual report & financial statements, performance presentations, press releases, etc., and external sources including journals, news & articles, government publications, competitor publications, sector reports, third-party database, and other credible publications.
Step 2: Market Segmentation:
After obtaining the historical market size of the artificial intelligence in drug discovery market, we conducted a detailed secondary analysis to gather historical market insights and share for different segments for major regions. Major segments included in the report are technology, components, drug type, application, therapeutic area, and end-user. Further country-level analyses were conducted to evaluate the overall adoption of Artificial Intelligence in Drug Discovery in every region.
Step 3: Factor Analysis:
After acquiring the historical market size of different segments and sub-segments, we conducted a detailed factor analysis to estimate the current market size of Artificial Intelligence in Drug Discovery. Further, we conducted factor analysis using dependent and independent variables such as growing incidences of rare diseases and the reducing cost of R&D activities by lowering the risks of failure in clinical trials will upsurge the demand for artificial intelligence in drug discovery. A thorough analysis was conducted for demand and supply-side scenarios considering top partnerships, merger and acquisition, business expansion, and product launches in the artificial intelligence in drug discovery industry across the globe.
Current Market Size Estimate & Forecast
Current Market Sizing: Based on actionable insights from the above 3 steps, we arrived at the current market size, key players in the Artificial Intelligence in Drug Discovery market, and market shares of the segments. All the required percentage shares split, and market breakdowns were determined using the above-mentioned secondary approach and were verified through primary interviews.
Estimation & Forecasting: For market estimation and forecast, weights were assigned to different factors including drivers & trends, restraints, and opportunities available for the stakeholders. After analyzing these factors, relevant forecasting techniques i.e., the top-down/bottom-up approach was applied to arrive at the market forecast about 2027 for different segments and subsegments across the major markets globally. The research methodology adopted to estimate the market size encompasses:
- The industry’s market size, in terms of value (USD) and the adoption rate of Artificial Intelligence in Drug Discovery across the major markets domestically
- All percentage shares, splits, and breakdowns of market segments and sub-segments
- Key players in the Artificial Intelligence in Drug Discovery market in terms of services offered. Also, the growth strategies adopted by these players to compete in the fast-growing market
Market Size and Share Validation
Primary Research: In-depth interviews were conducted with the Key Opinion Leaders (KOLs) including Top Level Executives (CXO/VPs, Sales Head, Marketing Head, Operational Head, and Regional Head, Country Head, etc.) across major regions. Primary research findings were then summarized, and statistical analysis was performed to prove the stated hypothesis. Inputs from primary research were consolidated with secondary findings, hence turning information into actionable insights.
Split of Primary Participants in Different Regions
Data triangulation technique was employed to complete the overall market estimation and to arrive at precise statistical numbers of each segment and sub-segment of the Artificial Intelligence in Drug Discovery market. Data was split into several segments & sub-segments post studying various parameters and trends in the areas of Technology, Components, Drug Type, Application, Therapeutic Area, and end-user of the Artificial Intelligence in Drug Discovery market.
Main Objective of the Artificial Intelligence in Drug Discovery Market Study
The current & future market trends of Artificial Intelligence in Drug Discovery were pinpointed in the study. Investors can gain strategic insights to base their discretion for investments from the qualitative and quantitative analysis performed in the study. Current and future market trends were determined the overall attractiveness of the market at a regional level, providing a platform for the industrial participant to exploit the untapped market to benefit as a first-mover advantage. Other quantitative goals of the studies include:
- Analyze the current and forecast market size of Artificial Intelligence in Drug Discovery in terms of value (USD). Also, analyze the current and forecast market size of different segments and sub-segments
- Segments in the study include areas of Technology, Components, Drug Type, Application, Therapeutic Area, and end-user
- Define and analysis of the regulatory framework for the Artificial Intelligence in Drug Discovery industry
- Analyze the value chain involved with the presence of various intermediaries, along with analyzing customer and competitor behaviors of the industry
- Analyze the current and forecast market size of the Artificial Intelligence in Drug Discovery market for the major region
- Major regions studied in the report include North America (the United States and Canada), Europe (Germany, France, Italy, Spain, and United Kingdom), Asia-Pacific (China, Japan, India, and Australia), and Rest of the World
- Company profiles of the Artificial Intelligence in Drug Discovery market and the growth strategies adopted by the market players to sustain in the fast-growing market
- Deep dive regional level analysis of the industry