Data Science Impacting the Pharmaceutical Industry

Data Science Impacting the Pharmaceutical Industry

Part I: Drug Discovery Applications

RELEASE DATE
28-Aug-2020
REGION
Global
Research Code: D9B0-01-00-00-00
SKU: HC03332-GL-TR_24683

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Description

Data science and AI have the potential to transform drug discovery in terms of costs, speed and efficiency. With explosion in biomedical data, data sharing and analysis platforms have surged. AI technologies are moving to the next phase of advancements, and when combined with other emerging tech areas, AI is expected to witness a full fledged adoption by pharma and biotech companies in the next 4-5 years

RESEARCH: INFOGRAPHIC

This infographic presents a brief overview of the research, and highlights the key topics discussed in it.
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Table of Contents

The Strategic Imperative 8™

The Strategic Imperative 8™ (continued)

The Impact of the Top Three Strategic Imperatives on Data Science in Drug Discovery Industry

ABOUT THE GROWTH PIPELINE ENGINE™

Growth Opportunities Fuel the Growth Pipeline Engine™

Research Methodology

Research Methodology (continued)

1.1 Role of Big Data and AI in Drug Discovery

1.2 Advantages of Data Science Augmentation in Drug Discovery

1.3 Improvement in KPIs using AI/Big Data in Drug Discovery

1.4 Areas of Focus Using AI and Big Data in Drug Discovery

1.5 Challenges in Leveraging Big Data and AI In Drug Discovery

2.1 Data Science- Technology Architecture in Drug Discovery

2.2 Applications of AI In Drug Discovery

2.3 Emerging Technology Trends in Data Science Technologies in Drug Discovery

2.4 AI In Drug Discovery – Tech Convergence Areas to Explore

3.1 Evolving Landscape with Rise in Industry Partnerships and Investments

3.2 Ecosystem of Pharma and AI Companies for Drug Discovery

3.3 Key Technology Management Strategies

3.4 Highlights of AI Enabled Drug Discovery Partnerships

3.5 Highlights of Big Pharma Engagement & Investments in AI Drug Discovery

3.6 Scaling up Long Term Research Partnerships and JVs

3.7 Strengthen Market Position with Acquisitions and Licensing

3.8 Accelerate Large Scale Data Sharing via Consortia

4.1 Atomwise

4.2 Exscientia

4.3 Insilico Medicine

4.4 BERG AI

4.5 Lantern Pharma

4.6 Cyclica

4.7 Recursion Pharma

4.8 nference

5.1 Disease Focus Areas for AI enabled Drug Discovery

5.2 Applications of AI and Big Data in Oncology Precision Medicine

5.3 Strategic Imperatives for AI Enabled Oncology Precision Medicine

5.4 Applications in Neurology/Neurodegenerative Disorders

5.5 Strategic Imperatives for AI Enabled Drug Discovery for Neurological/Neurodegenerative Diseases

5.6 Applications in Infectious Diseases/COVID-19 Drug Discovery and Repurposing

5.7 Strategic Imperatives for AI Enabled Drug Discovery for Infectious Diseases/SARS-CoV-2

5.8 Accelerating COVID-19 Drug Discovery with AI and Data Science

5.9 Applications in Orphan Diseases

6.1 Growth Opportunity 1: Drug “Repurposing” Using AI and Big Data

6.1 Growth Opportunity 1: Drug “Repurposing” Using AI and Big Data (continued)

6.2 Growth Opportunity 2: Lead Optimization Using AI – Drug Property and Bioactivity Prediction

6.3 Growth opportunity 2: Use of AI for Drug Property and Bioactivity Prediction Could Potentially Reduce Number of Failures in Clinical Development

6.4 Growth opportunity 3: Identify novel candidates and De novo drug synthesis using AI

6.4 Growth Opportunity 3: Identify Novel Candidates and De Novo Drug Design Using AI (continued)

7.1 IP Overview of AI Enabled Drug Discovery

7.2 Top Pharmaceutical/Biotech Patent Holders

8.1 Types of AI algorithms

9.1 Key Industry Contacts

9.1 Key Industry Contacts (continued)

10.1 YOUR NEXT STEPS

10.2 WHY FROST, WHY NOW?

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