Artificial Intelligence Revolutionizing the Pharmaceutical Industry

Artificial Intelligence Revolutionizing the Pharmaceutical Industry

Optimal Synergy Between Leading-edge Computational Science and Therapeutics Development

RELEASE DATE
13-Nov-2018
REGION
Global
Research Code: D815-01-00-00-00
SKU: HC03105-GL-TR_22531

$4,950.00

Special Price $3,712.50 save 25 %

In stock
SKU
HC03105-GL-TR_22531

$4,950.00

$3,712.50 save 25 %

DownloadLink

Pay by invoice

ENQUIRE NOW

Description

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.

Table of Contents

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

2.1 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

3.1 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

4.1 Companies Succeeding in AI-driven Drug Development

4.2 Companies Succeeding in AI-driven Data Leverage

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

6.1 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

7.1 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

8.1 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

9.1 Industry Interactions (continued)

9.1 Industry Interactions (continued)

9.1 Industry Interactions (continued)

9.1 Industry Interactions (continued)

9.1 Industry Interactions (continued)

10.1 Frost & Sullivan’s Coverage: Ongoing Research

Legal Disclaimer

Related Research
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.
More Information
No Index No
Podcast No
Industries Healthcare
WIP Number D815-01-00-00-00
Is Prebook No