Deep Learning in Drug Discovery market, Industry Analysis, and Forecast to 2035

Key Market Insights

Presently, more than 70 players across the globe claim to offer deep learning technologies 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 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

3.2.1. Teaching Machines

3.2.1.1. Machines for Computing

3.2.1.2. Artificial Intelligence

3.3. The Big Data Revolution

3.3.1. Overview of Big Data

3.3.2. Role of Internet of Things (IoT)

3.3.3. Key Application Areas of Big Data

3.3.3.1. Big Data Analytics in Healthcare

3.3.3.2. Machine Learning

3.3.3.3. Deep Learning

3.4. Deep Learning in Healthcare

3.4.1. Personalized Medicine

3.4.2. Lifestyle Management

3.4.3. Drug Discovery

3.4.4. Clinical Trial Management

3.4.5. Diagnostics

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

4.2.1. Analysis by Year of Establishment

4.2.2. Analysis by Company Size

4.2.3. Analysis by Location of Headquarters

4.2.4. Analysis by Application Area

4.2.5. Analysis by Focus Area

4.2.6. Analysis by Therapeutic Area

4.2.7. Analysis by Operational Model

4.2.7.1. Analysis by Service Centric Model

4.2.7.2. Analysis by Product Centric Model

5. MARKET OVERVIEW: DEEP LEARNING IN DIAGNOSTICS

5.1. Chapter Overview

5.2. Deep Learning in Diagnostics: Overall Market Landscape of Service / Technology Providers

5.2.1. Analysis by Year of Establishment

5.2.2. Analysis by Company Size

5.2.3. Analysis by Location of Headquarters

5.2.4. Analysis by Application Area

5.2.5. Analysis by Focus Area

5.2.6. Analysis by Therapeutic Area

5.2.7. Analysis by Type of Offering / Solution

5.2.8. Analysis by Compatible Device

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. PORTERS FIVE FORCES ANALYSIS

7.1. Chapter Overview

7.2. Methodology and Assumptions

7.3. Key Parameters

7.3.1. Threats of New Entrants

7.3.2. Bargaining Power of Companies Using Deep Learning for Drug Discovery and Diagnostics

7.3.3. Bargaining Power of Drug Developers

7.3.4. Threats of Substitute Technologies

7.3.5. Rivalry Among Existing Competitors

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

8.3.1. Analysis by Trial Registration Year

8.3.2. Analysis by Trial Status

8.3.3. Analysis by Trial Registration Year and Patient Enrollment

8.3.4. Analysis by Trial Registration Year and Trial Status

8.3.5. Analysis by Type of Sponsor / Collaborator

8.3.6. Analysis by Therapeutic Area

8.3.7. Word Cloud: Trial Focus Area

8.3.8. Analysis by Study Design

8.3.9. Geographical Analysis by Number of Clinical Trials

8.3.10. Geographical Analysis by Trial Registration Year and Patient Population

8.3.11. Leading Organizations: Analysis by Number of Registered Trials

9. FUNDING AND INVESTMENT ANALYSIS

9.1. Chapter Overview

9.2. Types of Funding

9.3. Deep Learning Market: Funding and Investment Analysis

9.3.1. Analysis by Year of Funding

9.3.2. Analysis by Amount Invested

9.3.3. Analysis by Type of Funding

9.3.4. Analysis by Year and Type of Funding

9.3.5. Analysis by Focus Areas

9.3.6. Analysis by Therapeutic Area

9.3.7. Analysis by Geography

9.3.8. Most Active Players: Analysis by Number of Funding Instances

9.3.9. Most Active Players: Analysis by Amount Invested

9.3.10. Most Active Investors: Analysis by Number of Funding Instances

10. START-UP HEALTH INDEXING

10.1. Chapter Overview

10.2. Start-ups Focused on Deep Learning in Drug Discovery

10.2.1. Methodology and Key Parameters

10.2.2. Analysis by Location of Headquarters

10.3. Benchmarking Analysis of Start-ups Focused on Deep Learning in Drug Discovery

10.3.1. Analysis by Focus Area

10.3.2. Analysis by Therapeutic Area

10.3.3. Analysis by Operational Model

10.3.4. Start-up Health Indexing: Roots Analysis Perspective

10.4. Start-ups Focused on Deep Learning in Diagnostics

10.4.1. Methodology and Key Parameters

10.4.2. Analysis by Location of Headquarters

10.5. Benchmarking Analysis of Start-ups Focused on Deep Learning in Diagnostics

10.5.1. Analysis by Focus Area

10.5.2. Analysis by Therapeutic Area

10.5.3. Analysis by Compatible Device

10.5.4. Analysis by Type of Offering

10.5.5. Start-up Health Indexing: Roots Analysis Perspective

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

12.3.1. Deep Learning in Drug Discovery Market: Analysis by Therapeutic Area, 2023-2035

12.3.2. Deep Learning in Drug Discovery Market: Analysis by Geography, 2023-2035

12.3.3. Deep Learning in Drug Discovery: Cost Saving Analysis

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

13.3.1. Deep Learning in Diagnostics Market: Analysis by Therapeutic Area, 2023-2035

13.3.2. Deep Learning in Diagnostics Market: Analysis by Geography, 2023-2035

14. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS

14.1. Chapter Overview

14.2. Sean Lane, Chief Executive Officer (Olive)

14.3. Junaid Kalia, Founder (NeuroCare.AI) and Adeel Memon, Assistant Professor, Neurology Specialist (West Virginia University Hospitals)

14.4. David Reich, President / Chief Operating Officer (The Mount Sinai Hospital) and Robbie Freeman, Vice President of Clinical Innovation (The Mount Sinai Hospital)

14.5. Elad Benjamin, Vice President, Business Leader Clinical Data Services (Philips) and Jonathan Laserson, Senior Deep Learning Researcher (Apple)

14.6. Kevin Lyman, Founder and Chief Science Officer (Enlitic)

15. CONCLUDING REMARKS

16. INTERVIEW TRANSCRIPTS

16.1. Chapter Overview

16.2. Nucleai

16.2.2. Interview Transcript: Avi Veidman, Chief Executive Officer, Yoav Blum, Director of AI and Ken Bloom, Head of Pathology

16.3. Mediwhale

16.3.2. Interview Transcript: Kevin Choi, Chief Executive Officer

16.4. Arterys

16.4.2. Interview Transcript: Babak Rasolzadeh, Former Vice President of Product and Software Development

16.5. AlgoSurg