In April 2024, BioPharmaTrend published a comprehensive review on AI in drug discovery. The study focused on nine leading companies designing drug candidates de novo and maintaining internal pipelines.
Analyzing Patent Portfolios
To understand AI drug discovery’s patent strategies, analyzing patent portfolios is essential. Key questions arise:
- Are AI/ML technologies protected alongside conventional pharma technologies?
- Are there discernible trends over time?
Composition vs. AI/ML Protection
The review highlighted differences in patent filings. Out of 390 total filings, 33% were for AI/ML, indicating an emphasis on traditional drug composition.
Trends Over Time
Both AI/ML and conventional technology filings have increased since 2012. However, conventional filings dominate, possibly due to the companies’ initial focus.
Portfolio-Target Mapping
Patent filings often align with commercial priorities. A detailed target-based mapping shows varied protection strategies across targets like EGFR and PI3Kα.
Evaluating AI/ML vs. Conventional Filings
AI/ML filings often lack specific targets, raising questions about their strategic focus. This could be due to the broad applicability of AI/ML technologies.
Comparing with Overall Landscape
AI drug discovery companies have fewer filings than industry leaders. This gap presents opportunities for developing valuable patents for defensive and cross-licensing purposes.
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