Artificial Intelligence in Drug Discovery Market by Offering, Technology, Process, Application, Therapeutic Area, End User

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[196 Pages Report] The Artificial Intelligence in Drug Discovery Market size was estimated at USD 1.08 billion in 2023 and expected to reach USD 1.35 billion in 2024, at a CAGR 27.10% to reach USD 5.81 billion by 2030.

Artificial Intelligence in drug discovery refers to the application of machine learning algorithms and AI systems in the process of discovering, designing, and optimizing new drug compounds. These AI models play a pivotal role in streamlining the traditionally complex and time-consuming drug discovery process, thus facilitating advancements in the field of medicine. The market growth is propelled by the growing burden of chronic diseases worldwide and the rising adoption of AI across biopharmaceutical companies for heightened precision, speed, and effectiveness in drug discovery. Moreover, the increasing need to manage the large data generated during preclinical studies drives market growth. The need for more skilled AI professionals in healthcare and the high costs associated with implementing AI is influencing growth limitation. The limited availability of data sets is a pivotal challenge curtailing the growth of AI in drug discovery. The opportunities are poised in fields related to novel drug discovery mechanisms and personalized medicine. Technological advancement in the burgeoning areas of AI research for drug development creates a potentiality for enhanced drug discovery, disease understanding, and patient-specific treatments.

KEY MARKET STATISTICS
Base Year [2023]	USD 1.08 billion
Estimated Year [2024]	USD 1.35 billion
Forecast Year [2030]	USD 5.81 billion
CAGR (%)	27.10%

Offering: AI Software propose a revolutionary approach to drug discovery

Within the field of drug discovery, Artificial Intelligence (AI) offers a robust range of services that expedite the process, enhance accuracy, and ultimately improve outcomes. These services majorly include structural analysis, drug repositioning, and pharmacodynamics modeling. AI software has catalyzed a digital revolution in drug discovery. Distinct software solutions have surfaced as a product of integrating AI into drug discovery. These software include predictive analytics, molecular docking, precision medicine, and modeling and analysis software to speed up matching a patient to the most effective.

Technology: Growing adoption of context-aware processing in personalized therapeutic

Context-aware processing is personalized, with AI algorithms cross-referencing genetic data, biomarkers, and disease indicators to suggest potential drug targets or bespoke treatments. Machine learning, another AI subfield, facilitates intelligent, unprogrammed decisions, predicting compound traits, patient reactions, and enhancing drug design. Natural language processing, meanwhile, harnesses the power of human language for data mining, assimilating information from academic sources to fortify data inclusivity. Context-aware processing offers personalized therapeutic recommendations, whereas machine learning drives the optimization of drug design. Conversely, natural language processing leverages large datasets to identify novel drug-disease associations. Rather than working in isolation, these technologies have convergent potentials, promising precise, expedited drug discovery.

Process: Significant augmentation in the drug discovery process with computational prowess and predictive capabilities

In the Artificial Intelligence (AI) world in drug discovery, candidate selection and validation is a crucial step in robustly assessing the potential success of prospective drug candidates. AI algorithms analyze molecular structures, predict their effect, and determine their viability. The next step involves hit identification and prioritization, prepping a list of promising drug candidates derived from AI screening. These hits are prioritized based on potency, selectivity, and safety. Following hit identification, the hit-to-lead identification or lead generation stage focuses on transforming the 'hits' into 'leads,' i.e., potential drug candidates that can be further optimized. Here, AI helps to evaluate and optimize leads with medicinal chemists testing and optimizing compounds. The next segment represents lead optimization, where potential drug candidates are enhanced for improved activity, specificity, and safety. This stage necessitates advanced AI technology to predict potential side effects and methodology to enhance drug efficacy. The drug discovery process also encompasses target identification and selection, which involves the choice of disease-modifying targets for the drug. The final stage is target validation, which verifies the selected target's role in the progression of the disease and its potential to be modulated by a drug. Artificial Intelligence continues revolutionizing drug discovery by augmenting each step with computational power and predictive capabilities. It significantly enhances drug discovery's efficiency and potential to deliver life-saving drugs to the market faster.

Application: Growing usage of AI-designed small molecule drugs for human clinical trials.

Biologics molecular-targeted drugs leverage AI for speedier and more accurate optimization, with AlphaFold demonstrating considerable protein prediction capabilities, expediting drug discovery. AI algorithms enhance disease identification and assessment by decoding patterns more accurately, allowing earlier interventions. Safety, toxicity, and compliance checks during drug development leverage AI to foresee toxicities, augmenting safety and decreasing costs/ Small molecule drug discovery, usually time-consuming, is being revolutionized by AI. Amidst COVID-19, efficient vaccine design and optimization are critical and facilitated by AI-enabled identification of viral pathogenic regions. Thus, AI is pivotal for pharmaceutical innovations, aiding in identifying diseases, designing therapeutics, and ensuring safety compliance.

Therapeutic Area: Rising adoption of AI in the drug discovery for personalized cancer treatment.

Artificial intelligence(AI) has been emerging as a transformative tool in cardiovascular disease management, ranging from early detection to personalized medication production. AI applications are seeing increased use in immuno-oncology, where they help classify and predict treatment responses. Companies and researchers are using AI to revolutionize the understanding and treatment of metabolic diseases, from diabetes to obesity. AI's potential to aid in diagnosing and developing treatments for neurodegenerative diseases has been recognized across the sector.

End User: Increasing use of AI in the drug discovery by pharmaceutical and biotechnology companies to accelerate their drug discovery process

Contract research organizations(CROs) leverage AI to significantly augment their drug discovery services, offering high-quality and efficient outcomes. CROs dealing with AI-powered drug discovery generally prefer solutions designed to streamline their workflow, accelerate the speed of discovery, and minimize human errors. Pharmaceutical and biotechnology companies, leading drug discovery drivers, show considerable affinity towards AI. AI facilitates these industries in expediting the drug discovery process, predicting drug response, and reducing costs associated with drug failure.

Research centers and academic & government institutes are increasingly capitalizing on AI's potential in drug discovery. The preference here lies in AI's power to predict potential drug candidates, minimize trial and error instances, and absorb vast data for precise research. Although the degree of AI utilization varies among end users, its positive impact is unmistakable. AI's potential to revolutionize drug discovery through its precision, speed, and cost-effectiveness is increasingly recognized across the field.

Regional Insights

The U.S. stands at the forefront of integrating AI into drug discoveries, fuelled by an active start-up environment and robust governmental funding. Canada echoes this dedication with considerable investment in AI-driven discovery platforms. European countries, such as the UK, France, and Germany, are leveraging AI and data science to revolutionize drug discovery procedures, attributed to strategic collaboration between academic institutions and the pharmaceutical industry. With China, Japan, and India at the helm, Asia-Pacific offers compelling dynamics. China's massive AI investment, paired with Japan's excellence in pharmaceutical research, is fostering the adoption of AI in drug discovery. In India, governmental support and an expanding IT sector are moving towards AI in drug discoveries. The U.S., China, and EU lead in patent claims for AI drug discoveries, representing consistent innovation in their pharmaceutical industries.

FPNV Positioning Matrix

The FPNV Positioning Matrix is pivotal in evaluating the Artificial Intelligence in Drug Discovery Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).

Market Share Analysis

The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Artificial Intelligence in Drug Discovery Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

Key Company Profiles

The report delves into recent significant developments in the Artificial Intelligence in Drug Discovery Market, highlighting leading vendors and their innovative profiles. These include Aria Pharmaceuticals, Inc., Atomwise, Inc., BenevolentAI Limited, BenevolentAI SA, BioSymetrics Inc., BPGbio Inc., Butterfly Network, Inc., Cloud Pharmaceuticals, Inc., Cyclica Inc., Deargen Inc., Deep Genomics Incorporated, Envisagenics, Inc., Euretos Services BV, Exscientia PLC, Insilico Medicine, Insitro, Inc., International Business Machines Corporation, InveniAI LLC, Microsoft Corporation, Novartis AG, NVIDIA Corporation, Oracle Corporation, Owkin, Inc., Verge Genomics Inc., and XtalPi Inc..

Market Segmentation & Coverage

This research report categorizes the Artificial Intelligence in Drug Discovery Market to forecast the revenues and analyze trends in each of the following sub-markets:

Offering
- Services
- Software

Technology
- Context-Aware Processing
- Machine Learning
- Natural Language Processing

Process
- Candidate Selection & Validation
- Hit Identification & Prioritization
- Hit-to-lead Identification/ Lead generation
- Lead Optimization
- Target Identification & Selection
- Target Validation

Application
- Biologics Design & Optimization
- Disease Identification & Assessment
- Safety, Toxicity, & Compliance Assessment
- Small Molecule Design & Optimization
- Vaccine Design & Optimization

Therapeutic Area
- Cardiovascular Disease
- Immuno-Oncology
- Metabolic Diseases
- Neurodegenerative Diseases

End User
- Contract Research Organizations
- Pharmaceutical & Biotechnology Companies
- Research Centers and Academic & Government Institutes

Region
- Americas
  - Argentina
  - Brazil
  - Canada
  - Mexico
  - United States
    - California
    - Florida
    - Illinois
    - New York
    - Ohio
    - Pennsylvania
    - Texas
- Asia-Pacific
  - Australia
  - China
  - India
  - Indonesia
  - Japan
  - Malaysia
  - Philippines
  - Singapore
  - South Korea
  - Taiwan
  - Thailand
  - Vietnam

Europe, Middle East & Africa
- Denmark
- Egypt
- Finland
- France
- Germany
- Israel
- Italy
- Netherlands
- Nigeria
- Norway
- Poland
- Qatar
- Russia
- Saudi Arabia
- South Africa
- Spain
- Sweden
- Switzerland
- Turkey
- United Arab Emirates
- United Kingdom

The report offers valuable insights on the following aspects:

1. Market Penetration: It presents comprehensive information on the market provided by key players.

2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.

3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.

4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.

5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.

The report addresses key questions such as:

1. What is the market size and forecast of the Artificial Intelligence in Drug Discovery Market?

2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Artificial Intelligence in Drug Discovery Market?

3. What are the technology trends and regulatory frameworks in the Artificial Intelligence in Drug Discovery Market?

4. What is the market share of the leading vendors in the Artificial Intelligence in Drug Discovery Market?

5. Which modes and strategic moves are suitable for entering the Artificial Intelligence in Drug Discovery Market?

Product Code: MRR-031BF22F953D

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Demand to Control Drug Discovery Process and Reduce Cost
  - 5.1.1.2. Increasing Need to Manage the Large Data Generated During Preclinical Studies
  - 5.1.1.3. Increasing Adoption across Biopharmaceutical Companies
- 5.1.2. Restraints
  - 5.1.2.1. Unavailability of Skilled Professionals
- 5.1.3. Opportunities
  - 5.1.3.1. AI Cloud to Create a Streamlined and Automated Approach in Drug Discovery
  - 5.1.3.2. Increasingly Growing R&D Investments
- 5.1.4. Challenges
  - 5.1.4.1. Limited Availability of Data Sets
5.2. Market Segmentation Analysis
- 5.2.1. Offering: AI Software propose a revolutionary approach to drug discovery
- 5.2.2. Technology: Growing adoption of context-aware processing in personalized therapeutic
- 5.2.3. Process: Significant augmentation in the drug discovery process with computational prowess and predictive capabilities
- 5.2.4. Application: Growing usage of AI-designed small molecule drugs for human clinical trials.
- 5.2.5. Therapeutic Area: Rising adoption of AI in the drug discovery for personalized cancer treatment.
- 5.2.6. End User: Increasing use of AI in the drug discovery by pharmaceutical and biotechnology companies to accelerate their drug discovery process
5.3. Market Disruption Analysis
5.4. Porter's Five Forces Analysis
- 5.4.1. Threat of New Entrants
- 5.4.2. Threat of Substitutes
- 5.4.3. Bargaining Power of Customers
- 5.4.4. Bargaining Power of Suppliers
- 5.4.5. Industry Rivalry
5.5. Value Chain & Critical Path Analysis
5.6. Pricing Analysis
5.7. Technology Analysis
5.8. Patent Analysis
5.9. Trade Analysis
5.10. Regulatory Framework Analysis

6. Artificial Intelligence in Drug Discovery Market, by Offering

6.1. Introduction
6.2. Services
6.3. Software

7. Artificial Intelligence in Drug Discovery Market, by Technology

7.1. Introduction
7.2. Context-Aware Processing
7.3. Machine Learning
7.4. Natural Language Processing

8. Artificial Intelligence in Drug Discovery Market, by Process

8.1. Introduction
8.2. Candidate Selection & Validation
8.3. Hit Identification & Prioritization
8.4. Hit-to-lead Identification/ Lead generation
8.5. Lead Optimization
8.6. Target Identification & Selection
8.7. Target Validation

9. Artificial Intelligence in Drug Discovery Market, by Application

9.1. Introduction
9.2. Biologics Design & Optimization
9.3. Disease Identification & Assessment
9.4. Safety, Toxicity, & Compliance Assessment
9.5. Small Molecule Design & Optimization
9.6. Vaccine Design & Optimization

10. Artificial Intelligence in Drug Discovery Market, by Therapeutic Area

10.1. Introduction
10.2. Cardiovascular Disease
10.3. Immuno-Oncology
10.4. Metabolic Diseases
10.5. Neurodegenerative Diseases

11. Artificial Intelligence in Drug Discovery Market, by End User

11.1. Introduction
11.2. Contract Research Organizations
11.3. Pharmaceutical & Biotechnology Companies
11.4. Research Centers and Academic & Government Institutes

12. Americas Artificial Intelligence in Drug Discovery Market

12.1. Introduction
12.2. Argentina
12.3. Brazil
12.4. Canada
12.5. Mexico
12.6. United States

13. Asia-Pacific Artificial Intelligence in Drug Discovery Market

13.1. Introduction
13.2. Australia
13.3. China
13.4. India
13.5. Indonesia
13.6. Japan
13.7. Malaysia
13.8. Philippines
13.9. Singapore
13.10. South Korea
13.11. Taiwan
13.12. Thailand
13.13. Vietnam

14. Europe, Middle East & Africa Artificial Intelligence in Drug Discovery Market

14.1. Introduction
14.2. Denmark
14.3. Egypt
14.4. Finland
14.5. France
14.6. Germany
14.7. Israel
14.8. Italy
14.9. Netherlands
14.10. Nigeria
14.11. Norway
14.12. Poland
14.13. Qatar
14.14. Russia
14.15. Saudi Arabia
14.16. South Africa
14.17. Spain
14.18. Sweden
14.19. Switzerland
14.20. Turkey
14.21. United Arab Emirates
14.22. United Kingdom

15. Competitive Landscape

15.1. Market Share Analysis, 2023
15.2. FPNV Positioning Matrix, 2023
15.3. Competitive Scenario Analysis
- 15.3.1. Merck Enters Two Strategic Collaborations to Strengthen AI-driven Drug Discovery
- 15.3.2. Launch of Insilico's Phase II Program Highlights Generative AI Momentum
- 15.3.3. Google Cloud Launches AI-powered Solutions to Safely Accelerate Drug Discovery and Precision Medicine
15.4. Strategy Analysis & Recommendation

16. Competitive Portfolio

16.1. Key Company Profiles
16.2. Key Product Portfolio