약물 표적 발굴을 위한 AI : Innovation Insights

Artificial Intelligence (AI) is revolutionizing drug target identification by leveraging vast datasets and advanced algorithms to uncover novel therapeutic targets. AI techniques, such as machine learning and natural language processing, enable the analysis of complex biological data, identifying potential drug targets with higher accuracy and speed than traditional methods. These technologies can predict protein-ligand interactions, analyze genetic and epigenetic data, and identify biomarkers associated with diseases. AI-driven approaches also facilitate the repurposing of existing drugs for new indications by identifying previously unrecognized target interactions. The integration of AI in drug discovery accelerates the identification of viable targets, reduces costs, and enhances the overall efficiency of the drug development process. As a result, AI is becoming an indispensable tool in the pharmaceutical industry, driving innovation and improving the success rates of drug discovery programs.

The use of AI for drug target identification is a rapidly growing innovation, with a significant increase in patent filings in 2022 and 2023. This innovation is largely driven by universities and startups, particularly in South Asian regions. Major technology companies such as IBM, Google, and Microsoft are leading in patent filings, with Roche and Takeda Pharma being the only big pharma companies with limited patenting activity. Startups and small biotech companies are increasingly contributing to patent shares. Investment activity in AI for drug target identification has seen 432 deals totaling $69.2 billion, with the United States accounting for the majority of these deals. Hiring activity in this area is primarily led by big pharma companies like Amgen, Bristol-Myers Squibb, and Johnson & Johnson.

How is our 'State of Innovation intelligence 2024' report unique from other reports in the market?

Comprehensive & Granular Data - Unlike generic reports, ours provides in-depth patent analysis, drug pipelines, and clinical trial intelligence, enabling precise R&D and business strategies.

Regulatory & Clinical Insights - We track evolving regulatory frameworks and clinical advancements, helping you mitigate risks and accelerate market entry.

Investment-Focused Analysis - Our report includes detailed financial deal assessments and funding trends, identifying lucrative opportunities that many reports overlook.

Competitive Intelligence - We provide a deep dive into pharmaceutical leaders, biotech startups, and academia, helping you benchmark against competitors and uncover collaboration opportunities.

Actionable Decision-Making Support - Designed for strategic planning, our insights go beyond data presentation, offering practical guidance for investment, innovation, and market positioning.

We recommend this valuable source of information to anyone involved in -

Drug Development and Pharma/Biotech Companies - Value chain

Pharma/Drug Manufacturing Companies - Leaders and Startups

Business Development and Market Intelligence

Investment Analysts and Portfolio Managers

Professional Services - Investment Banks, PE/VC Firms

M&A/Investment, Management Consultants, and Consulting Firms

Key Highlights

• AI for drug target identification is a fast-growing innovation area, with patent filings increasing in 2022 and 2023.
• The innovation is largely driven by universities and startups, with South Asian geographies leading in patenting activity.
• Technology companies, including IBM, Google, and Microsoft, are leading the innovation activity.
• Roche and Takeda Pharma are the only two big pharma companies with limited patenting activity.
• Technology startups and small biotech have taken the lead in applying AI for drug target identification.
• There have been 432 deals in the AI for drug target identification sector, totaling $69.2 billion.

Scope

• Innovation Insights: innovation examples by each use cases segment of various sectors to present key trends.
• Key player: This represents a sample list of key players in each use case highlighted in the report.
• Startups: This represents a sample list of emerging starups in each use case highlighted in the report.
• University: This represents a sample list of leading universities in each use case highlighted in the report.

Reasons to Buy

• Comprehensive Market Insights - Gain a deep understanding of how AI is transforming drug target identification, including its use in analyzing complex biological data and predicting protein-ligand interactions.
• Stay Ahead of Cutting-Edge Innovations - Learn about the latest advancements in AI techniques, such as machine learning and natural language processing, that are revolutionizing drug discovery and uncovering novel therapeutic targets.
• Competitive Landscape Analysis - Examine how leading pharmaceutical companies, biotech firms, and academic institutions are leveraging AI in drug discovery, offering valuable insights for competitive benchmarking.
• Investment & Partnership Opportunities - Identify emerging trends in AI-driven drug development, including funding, licensing deals, and strategic collaborations, to support informed decisions in R&D and commercialization.
• Enhance R&D Strategy - Leverage AI-driven insights into target identification, biomarker analysis, and drug repurposing to optimize your drug discovery pipeline and accelerate the development of new treatments.
• Data-Driven Decision Making - Make strategic decisions backed by robust AI-based data, including intellectual property trends, clinical trial advancements, and AI adoption in drug development.

Table of Contents

Table of Contents

1. Innovation Insights

• 1.1 Innovation radar
• 1.2 Innovation s-curve
• 1.3 Innovation deep dive
• 1.4 Innovation deep dive - trending indications
• 1.5 Top companies Based on portfolio strength and temporal indicators

2. Competitive Insights

• 2.1 Key innovation leaders - big pharma
• 2.2.Key innovators - startups and small biotech
• 2.3 Key innovations - Universities and research institutions
• 2.4 Most cited patents
• 2.5 Insights from AI hub
• 2.6 Overview of AI technologies
• 2.7 Overview of drug targets

3.Market Insights

• 3.1 Deals
• 3.2. Key Acquirers
• 3.3 Deal type distribution
• 3.4 Geographical distribution
• 3.5 Top Hiring Companies