Artificial Intelligence (AI) in Drug Discovery Market-By Component (Software, Service), Technology (Machine learning {Deep Learning}), Application Type (Target Identification), Therapeutic Area (Oncology), End-use (CRO), Global Forecast, 2023-2032

Description

List of Tables

Global Artificial Intelligence (AI) in Drug Discovery Market will expand at 30.1% CAGR from 2023 to 2032, driven by the growing number of cross-industry collaborations and partnerships. The convergence of pharmaceutical expertise with AI-driven technologies enables faster and more efficient drug discovery processes. Collaborations facilitate the integration of AI algorithms into drug research, accelerating the identification of potential compounds and reducing development timelines. This synergy between different sectors fuels innovation, driving the growth of the AI in drug discovery industry.

Quoting an instance, in October 2023, Evozyn revealed the completion of an $81 million Series B funding round dedicated to supporting the advancement of its generative AI-powered drug discovery platform and product development efforts. This development comes on the heels of a productive partnership with NVIDIA earlier in 2023, during which the two companies jointly created an innovative AI model capable of designing therapeutic proteins.

The overall AI in drug discovery market is categorized based on component, technology, application, therapeutic area, end-use, and region.

The services segment could garner commendable gains throughout 2023-2032. AI in drug discovery services is in high demand due to their ability to significantly expedite the drug development process. AI algorithms can analyze vast datasets, predict potential drug candidates, and optimize molecular structures with remarkable speed and precision. This not only reduces research and development timelines but also lowers costs. With the need for faster and more efficient drug discovery solutions, the demand for AI services in this sector continues to grow.

AI in drug discovery solutions and services could experience surging demand in preclinical testing applications by 2032 because they enhance accuracy and efficiency. AI-powered algorithms can swiftly analyze complex biological and chemical data, aiding in the identification of promising drug candidates and predicting their behavior in preclinical testing. This reduces the need for extensive laboratory experiments, accelerates the drug development timeline, and ultimately lowers costs, making AI indispensable in the preclinical testing phase of drug discovery.

Europe AI in drug discovery industry could exhibit noteworthy CAGR between 2023-2032. Europe boasts a strong pharmaceutical sector and is investing significantly in AI research. According to EFPIA's Annual Report for 2022, the research-based pharmaceutical industry in Europe has seen substantial growth in production value, rising from 127.5 billion euros in 2000 to 300 billion euros in 2021. Equally noteworthy is the remarkable increase in exports, which surged from 90.9 billion euros to 565 billion euros during the same timeframe. In 2021, imports accounted for 390 billion euros, resulting in a positive trade balance of 175 billion euros.

Regulatory support for innovation and partnerships between academia, startups, and established pharmaceutical companies fosters AI adoption. Additionally, the need for more efficient drug development processes to combat diseases like COVID-19 accelerates the demand for AI solutions in drug discovery across the European region.

Product Code: 6361

Chapter 1 Methodology & Scope

1.1 Market definition
1.2 Base estimates & calculations
1.3 Forecast parameters
1.4 COVID-19 impact analysis at global level
1.5 Data validation
1.6 Data Sources
- 1.6.1 Primary
- 1.6.2 Secondary
  - 1.6.2.1 Paid sources
  - 1.6.2.2 Public sources

Chapter 2 Executive Summary

2.1 Global artificial intelligence in drug discovery industry 360 degree synopsis, 2018 - 2032
- 2.1.1 Business trends
- 2.1.2 Regional trends
- 2.1.3 Component trends
- 2.1.4 Technology trends
- 2.1.5 Application type trends
- 2.1.6 Therapeutic area trends
- 2.1.7 End-use trends

Chapter 3 Artificial Intelligence in Drug Discovery Industry Insights

3.1 Industry ecosystem analysis
3.2 Industry impact forces
- 3.2.1 Growth drivers
  - 3.2.1.1 Growing number of cross industry collaborations and partnerships
  - 3.2.1.2 Artificial intelligence reduces cost and time utilized in the drug discovery& development process
  - 3.2.1.3 Rising prevalence of chronic and infectious diseases
- 3.2.2 Industry pitfalls & challenges
  - 3.2.2.1 Lack of data sets in the field of drug discovery
  - 3.2.2.2 Limited understanding and expertise
3.3 Growth potential analysis
- 3.3.1 By component
- 3.3.2 By technology
- 3.3.3 By application type
- 3.3.4 By therapeutic area
- 3.3.5 By end-use
3.4 COVID- 19 impact analysis
3.5 Regulatory landscape
3.6 AI in drug discovery - Drugs by stage and therapeutic area
3.7 AI in drug discovery funding, 2018-2020
3.8 Porter's analysis
3.9 PESTEL analysis

Chapter 4 Competitive Landscape, 2022

4.1 Introduction
4.2 Investment and partnership landscape
4.3 Company matrix analysis, 2022
4.4 Competitive analysis of major market players
4.5 Competitive positioning matrix
4.6 Strategy dashboard, 2022

Chapter 5 Artificial Intelligence in Drug Discovery Market Size and Forecast, By Component (USD Million)

5.1 Key trends, by component
5.2 Software
5.3 Services

Chapter 6 Artificial Intelligence in Drug Discovery Market Size and Forecast, By Technology (USD Million)

6.1 Key trends, by technology
6.2 Machine Learning
- 6.2.1 Deep Learning
- 6.2.2 Supervised Learning
- 6.2.3 Unsupervised Learning
- 6.2.4 Other Machine Learning Technologies
6.3 Other Technologies

Chapter 7 Artificial Intelligence in Drug Discovery Market Size and Forecast, By Application Type (USD Million)

7.1 Key trends, by application type
7.2 Molecular library screening
7.3 Target Identification
7.4 Drug Optimization and Repurposing
7.5 De novo Drug Designing
7.6 Preclinical Testing

Chapter 8 Artificial Intelligence in Drug Discovery Market Size and Forecast, By Therapeutic Area (USD Million)

8.1 Key trends, by therapeutic area
8.2 Oncology
8.3 Neurodegenerative Diseases
8.4 Inflammatory
8.5 Infectious Diseases
8.6 Metabolic Diseases
8.7 Rare Diseases
8.8 Cardiovascular Diseases
8.9 Other Therapeutic Areas

Chapter 9 Artificial Intelligence in Drug Discovery Market Size and Forecast, By End-use (USD Million)

9.1 Key trends, by end-use
9.2 Pharmaceutical and Biotechnology Companies
9.3 Contract Research Organization (CROs)
9.4 Other End-users

Chapter 10 Artificial Intelligence in Drug Discovery Market Size and Forecast, By Region (USD Million)

10.1 Key trends, by region
10.2 North America
- 10.2.1 U.S.
- 10.2.2 Canada
10.3 Europe
- 10.3.1 Germany
- 10.3.2 UK
- 10.3.3 France
- 10.3.4 Spain
- 10.3.5 Italy
- 10.3.6 Rest of Europe
10.4 Asia Pacific
- 10.4.1 Japan
- 10.4.2 China
- 10.4.3 India
- 10.4.4 Australia
- 10.4.5 South Korea
- 10.4.6 Rest of Asia Pacific
10.5 Latin America
- 10.5.1 Brazil
- 10.5.2 Mexico
- 10.5.3 Rest of Latin America
10.6 Middle East & Africa
- 10.6.1 South Africa
- 10.6.2 Saudi Arabia
- 10.6.3 Rest of Middle East & Africa

Chapter 11 Company Profiles

11.1 Microsoft
11.2 BenevolentAI
11.3 Exscientia
11.4 IBM Corporation
11.5 Alphabet (DeepMind)
11.6 NVIDIA Corporation
11.7 Deep Genomic
11.8 Atomwise Inc.
11.9 Cyclica
11.10 Deargen