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Data Science Impacting the Pharmaceutical Industry

  • Report
  • 73 Pages
  • August 2020
  • Region: Global
  • Frost & Sullivan
  • ID: 5144609

Part I: Drug Discovery Applications

Data science and AI have the potential to transform drug discovery in terms of costs, speed and efficiency. With the 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

Table of Contents

1.0 Strategic Imperatives
1.1 The Strategic Imperative 8™
1.2 The Impact of the Top Three Strategic Imperatives on Data Science in Drug Discovery Industry
1.3 About the Growth Pipeline Engine™
1.4 Growth Opportunities Fuel the Growth Pipeline Engine™
1.5 Research Methodology
2.0 Need for AI/Data Science in Drug Discovery
2.1 Role of Big Data and AI in Drug Discovery
2.2 Advantages of Data Science Augmentation in Drug Discovery
2.3 Improvement in KPIs using AI/Big Data in Drug Discovery
2.4 Areas of Focus Using AI and Big Data in Drug Discovery
2.5 Challenges in Leveraging Big Data and AI In Drug Discovery
3.0 Data Sciences in Drug Discovery-Technology Landscape &Trends
3.1 Data Science- Technology Architecture in Drug Discovery
3.2 Applications of AI In Drug Discovery
3.3 Emerging Technology Trends in Data Science Technologies in Drug Discovery
3.4 AI In Drug Discovery - Tech Convergence Areas to Explore
4.0 Industry Landscape & Stakeholder Ecosystem
4.1 Evolving Landscape with Rise in Industry Partnerships and Investments
4.2 Ecosystem of Pharma and AI Companies for Drug Discovery
4.3 Key Technology Management Strategies
4.4 Highlights of AI-Enabled Drug Discovery Partnerships
4.5 Highlights of Big Pharma Engagement & Investments in AI Drug Discovery
4.6 Scaling up Long Term Research Partnerships and JVs
4.7 Strengthen Market Position with Acquisitions and Licensing
4.8 Accelerate Large Scale Data Sharing via Consortia
5.0 Companies to Action
5.1 Atomwise
5.2 Exscientia
5.3 Insilico Medicine
5.5 Lantern Pharma
5.6 Cyclica
5.7 Recursion Pharma
5.8 nference
6.0 Impact of AI-Enabled Drug Discovery by Disease Applications
6.1 Disease Focus Areas for AI-enabled Drug Discovery
6.2 Applications of AI and Big Data in Oncology Precision Medicine
6.3 Strategic Imperatives for AI-Enabled Oncology Precision Medicine
6.4 Applications in Neurology/Neurodegenerative Disorders
6.5 Strategic Imperatives for AI-Enabled Drug Discovery for Neurological/Neurodegenerative Diseases
6.6 Applications in Infectious Diseases/COVID-19 Drug Discovery and Repurposing
6.7 Strategic Imperatives for AI-Enabled Drug Discovery for Infectious Diseases/SARS-CoV-2
6.8 Accelerating COVID-19 Drug Discovery with AI and Data Science
6.9 Applications in Orphan Diseases
7.0 Growth opportunities
7.1 Growth Opportunity 1: Drug “Repurposing” Using AI and Big Data
7.2 Growth Opportunity 2: Lead Optimization Using AI - Drug Property and Bioactivity Prediction
7.3 Growth opportunity 2: Use of AI for Drug Property and Bioactivity Prediction Could Potentially Reduce Number of Failures in Clinical Development
7.4 Growth opportunity 3: Identify novel candidates and De novo drug synthesis using AI
8.0 IP Analysis
8.1 IP Overview of AI-Enabled Drug Discovery
8.2 Top Pharmaceutical/Biotech Patent Holders
9.0 Appendix
9.1 Types of AI algorithms
10.0 Key Contacts
10.1 Key Industry Contacts
11.0 Next steps
11.1 Your Next Steps
11.2 Legal Disclaimer

Companies Mentioned

A selection of companies mentioned in this report includes:

  • Atomwise
  • Cyclica
  • Exscientia
  • Insilico Medicine
  • Lantern Pharma
  • nference
  • Recursion Pharma