Global Artificial Intelligence Revolutionizing the Pharmaceutical Industry Report 2018: Optimal Synergy Between Leading-edge Computational Science and Therapeutics Development
DUBLIN, Dec 4, 2018 /PRNewswire/ --
The "Artificial Intelligence Revolutionizing the Pharmaceutical Industry" report has been added to ResearchAndMarkets.com's offering.
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The impressive advances in life sciences research and development (R&D) befallen in the past two-three years are playing a leading role in the transformation of the healthcare industry.
A myriad of new developments in the fields of gene and cell therapies, empowered with nanotechnology advances, omics technologies and novel smart molecules approaches, are extensively enlighten drug discovery and development landscape for the effective treatment of diseases. AI is pursued to provide the best suited approach to leverage scientific literature, patient's omics-data and overall clinical data, to drive smart decisions.
Key Topics Covered:
1. Executive Summary
1.1 Pharmaceutical Industry - Facts and Concerns
1.2 Research Focus: Forwarding New Therapeutics
1.3 Research Scope: Unveiling AI-driven Technology
1.4 Analysis Framework: Frost & Sullivan Core Value
1.5 Research Methodology: Five Steps Toward Success
2. Technology Snapshot and Trend
2.1 Key Elements of Analysis: The AI' Concept
2.2 Brief Overview of AI in the Pharmaceutical Industry
2.3 The Evolution of Artificial Intelligence
2.4 The Science and Engineering Behind AI
2.5 Deep Learning and Machine Learning Approaches
3. Technology Status Review and Assessment
3.1 Growth Opportunities for AI Strategic Imperatives
3.2 AI Technology Segmentation
3.3 Additional AI Technology Segmentation
3.4 AI-driven Evolution in Healthcare Applications
3.5 AI-driven Pharmaceutical Applications
3.6 Utilization of AI-driven Databases
4. Artificial Intelligence Business Landscape
4.1 Companies Succeeding in AI-driven Drug Development
4.2 Companies Succeeding in AI-driven Data Leverage
5. Artificial Intelligence Benchmarking Model
5.1 AI-based Therapeutics Value Chain and Participants
5.2 Frost & Sullivan's Assessment Methodology
5.3 Frost & Sullivan's Innovation Identification
5.4 Technology Transfer Assessment and Perceptions
6. Artificial Intelligence Innovation Scorecard
6.1 Berg: Innovation Dashboard
6.2 Berg: Process Qualification
6.3 Berg: Application Prioritization
6.4 Berg: Main Prioritization Features
6.5 BenevolentAI: Innovation Dashboard
6.6 BenevolentAI: Process Qualification
6.7 BenevolentAI: Application Prioritization
6.8 BenevolentAI: Main Prioritization Features
6.9 Kyndi: Innovation Dashboard
6.10 Kyndi: Innovation Framework
6.11 Kyndi: Process Qualification
6.12 Kyndi: Application Prioritization
6.13 Kyndi: Main Prioritization Features
6.14 Evid Science: Innovation Dashboard
6.15 Evid Science: Process Qualification
6.16 Evid Science: Application Prioritization
6.17 Evid Science: Main Prioritization Features
6.18 ReviveMed: Innovation Dashboard
6.19 ReviveMed: Process Qualification
6.20 ReviveMed: Application Prioritization
6.21 ReviveMed: Main Features Prioritization
6.22 Structura Bio: Innovation Dashboard
6.23 Structura Bio: Process Qualification
6.24 Structura Bio: Application Prioritization
6.25 Structura Bio: Main Features Prioritization
6.26 AcuraStem: Innovation Dashboard
6.27 AcuraStem: Process Qualification
6.28 AcuraStem: Application Prioritization
6.29 AcuraStem: Main Features Prioritization
6.30 FDNA: Innovation Dashboard
6.31 FDNA: Process Qualification
6.32 FDNA: Application Prioritization
6.33 FDNA: Main Features Prioritization
6.34 Innoplexus: Innovation Dashboard
6.35 Innoplexus: Process Qualification
6.36 Inoplexus: Application Prioritization
6.37 Innoplexus: Main Features Prioritization
6.38 Biovista: Innovation Dashboard
6.39 Biovista: Process Qualification
6.40 Biovista: Application Prioritization
6.41 Biovista: Main Features Prioritization
6.42 Standigm: Innovation Dashboard
6.43 Standigm: Process Qualification
6.44 Standigm: Application Prioritization
6.45 Standigm: Main Features Prioritization
7. Funding and Investment
7.1 Funding and Investment Models and Adoption
7.2 Funding and Investment Trends
7.3 Partnership Collaborations Advancing AI Pharma
7.4 Companies Raising Funding for AI Developments
8. Technology Trends and Roadmapping
8.1 Technology Maturity Level and Description
8.2 Roadmap Tapping into Technology Synergy
8.3 Business Model Hybridization
8.4 Future Perspective for AI-driven Therapeutics
9. Key Industry Influencers
10. Appendix
For more information about this report visit https://www.researchandmarkets.com/research/gpt93b/global_artificial?w=5
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