Key Market Insights
 Presently, more than 70 players across the globe claim to offer deep learning technology for potential applications across various steps of drug discovery and development process
 Majority (70%) of the stakeholders employ proprietary deep learning-based technologies in drug discovery to offer big data analysis
 Nearly 50% of the deep learning-based diagnostic providers are based in North America; most such players offer technologies for use across medical imaging and medical diagnosis related applications
 Around 70% of the players engaged in offering deep learning solutions for diagnostics have been established post-2011; majority of the players offer solutions focused on oncological disorders
 Foreseeing the lucrative potential, a large number of players have made investments worth over USD 15 billion, across 210 funding instances, to advance the initiatives undertaken by deep learning industry stakeholders
 Over the past few years, more than 704,000 patients have been recruited / enrolled in clinical trials registered for deep learning-based solutions / diagnostics across different geographies
 Our proprietary benchmarking analysis, based on a variety of parameters, indicates the leading start-ups / small firms that are spearheading innovation in this domain
 Some players have managed to establish strong competitive positions; in the near future, we expect multiple acquisitions to take place wherein the relative valuation of a firm is likely to be a key determinant
 Increasing adoption of deep learning technologies in the life sciences and healthcare industry is anticipated to create profitable business opportunities for the technology developers
 The market opportunity associated with deep learning in drug discovery is expected to witness an annualized growth rate of 23% over the coming 12 years
 In the long term, the opportunity for deep learning in diagnostics is projected to grow exponentially; the market is likely to be well distributed across various therapeutic areas and geographical regions

Table of Contents

1. PREFACE
1.1. Introduction
1.2. Key Market Insights
1.3. Scope of the Report
1.4. Research Methodology
1.5. Frequently Asked Questions
1.6. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION
3.1. Humans, Machines and Intelligence
3.2. The Science of Learning
3.3. The Big Data Revolution
3.4. Deep Learning in Healthcare
3.5. Concluding Remarks

4. MARKET OVERVIEW: DEEP LEARNING IN DRUG DISCOVERY
4.1. Chapter Overview
4.2. Deep Learning in Drug Discovery: Overall Market Landscape of Service / Technology Providers

5. MARKET OVERVIEW: DEEP LEARNING IN DIAGNOSTICS
5.1. Chapter Overview
5.2. Deep Learning in Diagnostics: Overall Market Landscape of Service / Technology Providers

6. COMPANY PROFILES
6.2. Aegicare
6.2.1. Company Overview
6.2.2. Service Portfolio
6.2.3. Recent Developments and Future Outlook
6.3. Aiforia Technologies
6.4. Ardigen
6.5. Berg
6.6. Google
6.7. Huawei
6.8. Merative
6.9. Nference
6.10. Nvidia
6.11. Owkin
6.12. Phenomic AI
6.13. Pixel AI

7. PORTER’S FIVE FORCES ANALYSIS
7.1. Chapter Overview
7.2. Methodology and Assumptions
7.3. Key Parameters
7.4. Concluding Remarks

8. CLINICAL TRIAL ANALYSIS
8.1. Chapter Overview
8.2. Scope and Methodology
8.3 Deep Learning Market: Clinical Trial Analysis

9. FUNDING AND INVESTMENT ANALYSIS
9.1. Chapter Overview
9.2. Types of Funding
9.3. Deep Learning Market: Funding and Investment Analysis

10. START-UP HEALTH INDEXING
10.1. Chapter Overview
10.2. Start-ups Focused on Deep Learning in Drug Discovery
10.3. Benchmarking Analysis of Start-ups Focused on Deep Learning in Drug Discovery
10.4. Start-ups Focused on Deep Learning in Diagnostics
10.5. Benchmarking Analysis of Start-ups Focused on Deep Learning in Diagnostics
10.5.1. Analysis by Focus Area

11. COMPANY VALUATION ANALYSIS
11.1. Chapter Overview
11.2. Company Valuation Analysis: Key Parameters
11.3. Methodology
11.4. Company Valuation Analysis: Roots Analysis Proprietary Scores

12. MARKET SIZING AND OPPORTUNITY ANALYSIS: DEEP LEARNING IN DRUG DISCOVERY
12.1. Chapter Overview
12.2. Key Assumptions and Methodology
12.3. Overall Deep Learning in Drug Discovery Market, 2023-2035

13. MARKET SIZING AND OPPORTUNITY ANALYSIS: DEEP LEARNING IN DIAGNOSTICS
13.1. Chapter Overview
13.2. Key Assumptions and Methodology
13.3. Overall Deep Learning in Diagnostics Market, 2023-2035

14. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS

15. CONCLUDING REMARKS

16. APPENDIX 1: TABULATED DATA

17. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS

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https://www.rootsanalysis.com/reports/view_document/deep-learning-in-drug-discovery-market/156.html

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