Artificial Intelligence In Drug Discovery Market Size, Share & Trends Analysis Report By Application (Drug Optimization & Repurposing, Preclinical Testing), By Therapeutic Area, By Region, And Segment Forecasts, 2023 - 2030

Research Methodology

A three-pronged approach was followed for deducing the artificial intelligence in drug discovery market estimates and forecasts. The process has three steps: information procurement, analysis, and validation. The whole process is cyclical, and steps repeat until the estimates are validated. The three steps are explained in detail below:

Information procurement: Information procurement is one of the most extensive and important stages in our research process, and quality data is critical for accurate analysis. We followed a multi-channel data collection process for artificial intelligence in drug discovery market to gather the most reliable and current information possible.

• We buy access to paid databases such as Hoover’s and Factiva for company financials, industry information, white papers, industry journals, SME journals, and more.
• We tap into Grand View’s proprietary database of data points and insights from active and archived monitoring and reporting.
• We conduct primary research with industry experts through questionnaires and one-on-one phone interviews.
• We pull from reliable secondary sources such as white papers and government statistics, published by organizations like WHO, NGOs, World Bank, etc., Key Opinion Leaders (KoL) publications, company filings, investor documents, and more.
• We purchase and review investor analyst reports, broker reports, academic commentary, government quotes, and wealth management publications for insightful third-party perspectives.

Analysis: We mine the data collected to establish baselines for forecasting, identify trends and opportunities, gain insight into consumer demographics and drivers, and so much more. We utilized different methods of artificial intelligence in drug discovery market data depending on the type of information we’re trying to uncover in our research.

• Market Research Efforts: Bottom-up Approach for estimating and forecasting demand size and opportunity, top-down Approach for new product forecasting and penetration, and combined approach of both Bottom-up and Top-down for full coverage analysis.

• Value-Chain-Based Sizing & Forecasting: Supply-side estimates for understanding potential revenue through competitive benchmarking, forecasting, and penetration modeling.

• Demand-side estimates for identifying parent and ancillary markets, segment modeling, and heuristic forecasting.

• Qualitative Functional Deployment (QFD) Modelling for market share assessment.

Market formulation and validation: We mine the data collected to establish baselines for forecasting, identify trends and opportunities, gain insight into consumer demographics and drivers, and so much more. We utilize different methods of data analysis depending on the type of information we’re trying to uncover in our research.

• Market Formulation: This step involves the finalization of market numbers. This step on an internal level is designed to manage outputs from the Data Analysis step.

• Data Normalization: The final market estimates and forecasts are then aligned and sent to industry experts, in-panel quality control managers for validation.

• This step also entails the finalization of the report scope and data representation pattern.

• Validation: The process entails multiple levels of validation. All these steps run in parallel, and the study is forwarded for publishing only if all three levels render validated results.

Artificial Intelligence In Drug Discovery Market Categorization:

The artificial intelligence in drug discovery market was categorized into four segments, namely application (Drug Optimization and Repurposing, Preclinical Testing), therapeutic area (Oncology, Neurodegenerative Diseases, Cardiovascular Diseases, Metabolic Diseases, Infectious Diseases), component (Software, Hardware, Services), and regions (North America, Europe, Asia Pacific, Latin America, Middle East & Africa).

Segment Market Methodology:

The artificial intelligence in drug discovery market was segmented into application, therapeutic area, component, and regions. The demand at a segment level was deduced using a funnel method. Concepts like the TAM, SAM, SOM, etc., were put into practice to understand the demand. We at GVR deploy three methods to deduce market estimates and determine forecasts. These methods are explained below:

Market research approaches: Bottom-up

• Demand estimation of each product across countries/regions summed up to from the total market.

• Variable analysis for demand forecast.

• Demand estimation via analyzing paid database, and company financials either via annual reports or paid database.

• Primary interviews for data revalidation and insight collection.

Market research approaches: Top-down

• Used extensively for new product forecasting or analyzing penetration levels.

• Tool used invoice product flow and penetration models Use of regression multi-variant analysis for forecasting Involves extensive use of paid and public databases.

• Primary interviews and vendor-based primary research for variable impact analysis.

Market research approaches: Combined

• This is the most common method. We apply concepts from both the top-down and bottom-up approaches to arrive at a viable conclusion.

Regional Market Methodology:

The artificial intelligence in drug discovery market was analyzed at a regional level. The global was divided into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa, keeping in focus variables like consumption patterns, export-import regulations, consumer expectations, etc. These regions were further divided into twenty countries, namely, the U.S.; Canada; the UK.; Germany; France; Italy; Spain; Russia; Japan; China; India; South Korea; Australia; Singapore; Brazil; Mexico; Argentina; South Africa; Saudi Arabia; UAE.

All three above-mentioned market research methodologies were applied to arrive at regional-level conclusions. The regions were then summed up to form the global market.

Artificial intelligence in drug discovery market companies & financials:

The artificial intelligence in drug discovery market was analyzed via companies operating in the sector. Analyzing these companies and cross-referencing them to the demand equation helped us validate our assumptions and conclusions. Key market players analyzed include:

• IBM Corporation - IBM Corporation, a prominent global technology and consulting conglomerate, operates in five distinct business segments: Software, Global Business Services (GBS), Global Technology Services (GTS), Systems & Technology (STG), and Global Financing. Over the past decade, IBM has shifted its focus from commoditized markets, such as personal computers, to more lucrative and high-value sectors, such as data analytics, cloud computing, business intelligence, business continuity, virtualization, and security. In the healthcare industry, IBM offers solutions such as sustainable healthcare systems, collaborative care improvement, and enhanced healthcare access. IBM Cloud Solutions are provided through Watson Health, which integrates cognitive and cloud technologies to optimize performance and manage patient health. Watson Health operates in six primary areas: imaging, oncology and genomics, life sciences, government, value-based care, and consumer goods. Watson Health has served over 100,000 patients and consumers worldwide.

• Exscientia Limited - Exscientia Limited, a groundbreaking pharmatech company and a pioneering full-stack Artificial Intelligence (AI) drug discovery organization, boasts a unique capability to generate its own data before seamlessly integrating the analytical power of AI with the ingenuity and expertise of various scientific professionals. This innovative approach significantly reduces up to three quarters of stages in preclinical drug discovery, thereby expediting the delivery of new treatments to patients worldwide. Exscientia’s drug discovery platform is underpinned by best-in-class and validated proprietary AI technology. The company’s areas of expertise include bispecific drugs, drug design, small molecules, polypharmacology, data-driven drug design, and AI.

• GNS Healthcare - GNS Healthcare, based in Somerville, Massachusetts, is a prominent player in the healthcare industry, specializing in the development of causal AI and simulation technology solutions for the discovery and development of medicines. The company’s innovative offerings, such as the REFS causal AI and simulation platform and the Gemini in silico patient model, are designed to accelerate the development and discovery of drugs, ultimately reducing the overall cost of patient care and improving outcomes. GNS Healthcare collaborates with pharmaceutical and biotechnology companies, research centers, medical societies, and patient advocacy groups, and its technology can integrate and transform different patient data types into computer models, including bioinformatics, data analytics, big data, machine learning, and genomics data.

• Alphabet, Inc. - DeepMind, a subsidiary of Alphabet, Inc., is a leading research group in the field of artificial intelligence (AI). The company comprises a team of engineers, scientists, and machine learning experts who work collaboratively to advance AI capabilities. DeepMind collaborates with other companies to address critical challenges while ensuring safety and ethics are a top priority. The company’s long-term aim is to improve intelligence by developing general and capable problem-solving systems, known as Artificial General Intelligence (AGI). In 2014, DeepMind collaborated with Google to accelerate its work and set its research agenda. The company uses its broad spectrum of technologies for public benefits and scientific discovery. DeepMind’s significant research breakthroughs in AI include AlphaGo, Transformers, word2vec, WaveNet, AlphaFold, sequence to sequence models, distillation, deep reinforcement learning, and distributed systems and software frameworks like TensorFlow and JAX for expressing, training, and deploying large-scale ML models.

• BenevolentAI - BenevolentAI is a leading player in the healthcare industry, specializing in AI-enabled drug discovery. The company combines computational medicine and advanced AI with the principles of open systems and cloud computing to enhance design, development, and testing in drug production. BenevolentAI’s specialties include drug development, scientific innovation, machine learning, engineering, deep learning, bioscience, and big data. 

• BioSymetrics, Inc. - BioSymetrics, Inc. is a prominent biomedical AI company that is dedicated to accelerating early-stage drug discovery by leveraging AI to develop a comprehensive understanding of human health and pharmaceutical research. The company integrates clinical and experimental data using machine learning to translate human disease biology and advance precision medicines. BioSymetrics empowers research and development as well as healthcare innovation with its leading expertise in data science. The company’s specialties encompass a wide range of areas, including big data, predictive analytics, data analytics, machine learning, healthcare analytics, biomedical informatics, bioinformatics, computational biology, healthcare IT, software engineering, biomedicine, and digital health. 

• Euretos - Euretos is a leading AI platform that focuses on in silico discovery and validation of targets and biomarkers for preclinical researchers. The platform is also utilized by pharmaceutical, biotech, and academic institutions for multiomics research. Euretos integrates over 200 public databases with the latest multiomics data, interlinked with experimental, literature, and clinical evidence. The company’s specialties include target identification, life sciences data integration, biomarker discovery, drug repositioning, big data analytics, literature search, computational biology, and semantic integration. 

• BERG LLC - BERG LLC is a clinical-stage biotech company that leverages its proprietary platform, Interrogative Biology, to map diseases and enhance treatments across neurology, oncology, and rare diseases. The company combines traditional R&D methods with interrogative biology to develop a pipeline of first-in-class product candidates and diagnostics. BERG is focused on improving human health by combining AI and patient biology. The company’s specialties include biotechnology, AI drug discovery platform, AI/advanced analytics healthcare solutions, and cancer/metabolic disease research. 

• Atomwise, Inc. - Atomwise, Inc. is a prominent player in the field of AI-driven drug discovery. The company has developed AtomNet, a deep learning-driven computational platform for structure-based drug design, which includes a library of more than three trillion synthesizable compounds. Atomwise’s innovative approach has garnered significant attention, leading to partnerships with a wide range of organizations, including pharmaceutical companies, academic institutions, and research organizations.

• Insitro  - Insitro is a San Francisco-based drug discovery and development company that is at the forefront of leveraging machine learning and high-throughput biology to transform medicine development. The company applies in vitro strategies and machine learning to model the state of disease and design new therapeutic interventions for drug development. Insitro’s primary goal is to eliminate significant bottlenecks in traditional drug discovery, ultimately increasing the number of people receiving care at a lower cost.

Value chain-based sizing & forecasting

Supply Side Estimates

• Company revenue estimation via referring to annual reports, investor presentations, and Hoover’s.

• Segment revenue determination via variable analysis and penetration modeling.

• Competitive benchmarking to identify market leaders and their collective revenue shares.

• Forecasting via analyzing commercialization rates, pipelines, market initiatives, distribution networks, etc.

Demand side estimates

• Identifying parent markets and ancillary markets

• Segment penetration analysis to obtain pertinent

• revenue/volume

• Heuristic forecasting with the help of subject matter experts

• Forecasting via variable analysis

Artificial Intelligence In Drug Discovery Market Report Objectives:

• Understanding market dynamics (in terms of drivers, restraints, & opportunities) in the countries.

• Understanding trends & variables in the individual countries & their impact on growth and using analytical tools to provide high-level insights into the market dynamics and the associated growth pattern.

• Understanding market estimates and forecasts (with the base year as 2022, historic information from 2016 to 2021, and forecast from 2023 to 2030). Regional estimates & forecasts for each category are available and are summed up to form the global market estimates.

Artificial Intelligence In Drug Discovery Market Report Assumptions:

• The report provides market value for the base year 2022 and a yearly forecast till 2030 in terms of revenue/volume or both. The market for each of the segment outlooks has been provided on region & country basis for the above-mentioned forecast period.

• The key industry dynamics, major technological trends, and application markets are evaluated to understand their impact on the demand for the forecast period. The growth rates were estimated using correlation, regression, and time-series analysis.

• We have used the bottom-up approach for market sizing, analyzing key regional markets, dynamics, & trends for various products and end-users. The total market has been estimated by integrating the country markets.

• All market estimates and forecasts have been validated through primary interviews with the key industry participants.

• Inflation has not been accounted for to estimate and forecast the market.

• Numbers may not add up due to rounding off.

• Europe consists of EU-8, Central & Eastern Europe, along with the Commonwealth of Independent States (CIS).

• Asia Pacific includes South Asia, East Asia, Southeast Asia, and Oceania (Australia & New Zealand).

• Latin America includes Central American countries and the South American continent

• Middle East includes Western Asia (as assigned by the UN Statistics Division) and the African continent.

Primary Research

GVR strives to procure the latest and unique information for reports directly from industry experts, which gives it a competitive edge. Quality is of utmost importance to us, therefore every year we focus on increasing our experts’ panel. Primary interviews are one of the critical steps in identifying recent market trends and scenarios. This process enables us to justify and validate our market estimates and forecasts to our clients. With more than 8,000 reports in our database, we have connected with some key opinion leaders across various domains, including healthcare, technology, consumer goods, and the chemical sector. Our process starts with identifying the right platform for a particular type of report, i.e., emails, LinkedIn, seminars, or telephonic conversation, as every report is unique and requires a differentiated approach.

We send out questionnaires to different experts from various regions/ countries, which is dependent on the following factors:

• Report/Market scope: If the market study is global, we send questionnaires to industry experts across various regions, including North America, Europe, Asia Pacific, Latin America, and MEA.

• Market Penetration: If the market is driven by technological advancements, population density, disease prevalence, or other factors, we identify experts and send out questionnaires based on region or country dominance.

The time to start receiving responses from industry experts varies based on how niche or well-penetrated the market is. Our reports include a detailed chapter on the KoL opinion section, which helps our clients understand the perspective of experts already in the market space.