Biotech companies are pursuing three distinct strategies to move beyond expensive custom data generation: foundation model fine-tuning, data-efficient computational methods using existing databases and continued targeted investment in proprietary experimental datasets.
Success depends on matching computational strategy to specific therapeutic focus and resources and startups may favor foundation models or data-efficient approaches, while large pharma can integrate multiple methods across different programs.
Rather than following a single industry trend, companies should choose their AI approach based on their unique circumstances, as multiple viable paths exist for accelerating drug discovery while reducing R&D costs.

Mapping Biopharma’s AI Strategy: From Custom Datasets to Foundation Models

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