LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence

Researchers developed an LLM-based agent system for identifying competing drugs in clinical indications, achieving 83% recall compared to 65% and 60% for competitor systems. The agent validates results using an LLM-as-a-judge approach to minimize hallucinations, reducing biotech due diligence analysis time from 2.5 days to 3 hours in production deployment.

This research addresses a critical pain point in biotech investment analysis: accurately mapping competitive drug landscapes across fragmented, licensed data sources with inconsistent terminology. The challenge is substantial because drug information spans multiple registries with misaligned ontologies, competing products use numerous aliases, and the market evolves rapidly. Traditional manual analysis required extensive analyst time, creating bottlenecks in venture capital due diligence workflows.

The innovation lies in two technical components: an agent architecture that retrieves and normalizes drug competitor data despite fragmentation, and a validation layer using LLM-as-a-judge that filters hallucinations—a known weakness in language models that generate factual content. The team trained their system on five years of biotech VC memos, transforming unstructured diligence notes into a structured benchmark, effectively solving the absence of public evaluation standards for this task.

The performance metrics demonstrate meaningful improvement over existing commercial tools, with 83% recall suggesting the system captures most relevant competitors. The 20x reduction in analyst turnaround time—from 2.5 days to 3 hours—translates to tangible operational value for biotech investors, enabling faster investment decision-making and deal screening at scale.

The enterprise deployment signals market validation beyond academic merit. As biotech investment velocity increases and deal complexity grows, AI systems that accelerate due diligence become competitive advantages. The architecture combining retrieval, extraction, and validation through multiple agent passes offers a template for other high-stakes fact-finding domains requiring precision and recall balance.

• →LLM-agent system achieves 83% recall in competitive drug discovery, outperforming OpenAI Deep Research (65%) and Perplexity Labs (60%)
• →LLM-as-a-judge validation layer significantly reduces hallucinations and false positives in drug competitor identification
• →Production deployment reduced biotech due diligence turnaround time by approximately 20x, from 2.5 days to 3 hours
• →System addresses data fragmentation, ontology mismatches, and drug name aliases across licensed registries and paywalled sources
• →Multi-year VC memo dataset transformed into first public benchmark for competitive drug landscape evaluation