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

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