LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence 1. This paper presents a novel approach to mapping the competitive landscape for drug assets using LLM-based agents, achieving significant improvements in efficiency and accuracy for due diligence processes. 2. The system developed by the authors uses a competitor-discovery AI agent that retrieves all relevant drugs for a given indication and extracts key attributes, outperforming existing tools like OpenAI Deep Research and Perplexity Labs in terms of recall. 3. A major innovation is the introduction of a competitor validating LLM-as-a-judge agent, which filters out false positives to maximize precision and suppress hallucinations, ensuring high-quality outputs. 4. The study transforms five years of unstructured diligence memos into a structured evaluation corpus, creating a benchmark for assessing the performance of different models and agents in competitor discovery. 5. In a real-world case study with a biotech VC investment fund, the system reduced analyst turnaround time from 2.5 days to approximately 3 hours, demonstrating substantial operational impact and efficiency gains. 6. The authors compare various model configurations and show that scaffolded agents with multi-hop reasoning and web search capabilities significantly outperform single-pass prompting agents, especially on more difficult cases. 7. The system’s ability to handle multilingual, multimodal data and its robustness in dealing with fragmented and rapidly changing information landscapes highlight its potential for broader applications in the pharmaceutical industry. 8. The work underscores the importance of domain-specific benchmarks and the need for end-to-end, domain-grounded evaluation to ensure the stability and reliability of deployed AI systems in critical tasks like drug asset due diligence. 📜Paper: arxiv.org/abs/2508.16571 #LLM #DrugDiscovery #AIAgent #CompetitiveLandscape #DueDiligence #Biotech #Pharmaceuticals #Efficiency #Innovation