LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence (2508.16571v3)

Abstract: In this paper, we describe and benchmark a competitor-discovery component used within an agentic AI system for fast drug asset due diligence. A competitor-discovery AI agent, given an indication, retrieves all drugs comprising the competitive landscape of that indication and extracts canonical attributes for these drugs. The competitor definition is investor-specific, and data is paywalled/licensed, fragmented across registries, ontology-mismatched by indication, alias-heavy for drug names, multimodal, and rapidly changing. Although considered the best tool for this problem, the current LLM-based AI systems aren't capable of reliably retrieving all competing drug names, and there is no accepted public benchmark for this task. To address the lack of evaluation, we use LLM-based agents to transform five years of multi-modal, unstructured diligence memos from a private biotech VC fund into a structured evaluation corpus mapping indications to competitor drugs with normalized attributes. We also introduce a competitor validating LLM-as-a-judge agent that filters out false positives from the list of predicted competitors to maximize precision and suppress hallucinations. On this benchmark, our competitor-discovery agent achieves 83% recall, exceeding OpenAI Deep Research (65%) and Perplexity Labs (60%). The system is deployed in production with enterprise users; in a case study with a biotech VC investment fund, analyst turnaround time dropped from 2.5 days to $\sim$3 hours ($\sim$20x) for the competitive analysis.

• The paper presents an LLM-based system that automates competitive landscape mapping for drug asset due diligence with 83% recall.
• It utilizes a ReAct framework and hierarchical parsing to structure multi-modal data, achieving higher precision than baseline models.
• Deployment reduced analyst turnaround time from 2.5 days to 3 hours, demonstrating significant practical efficiency in due diligence.

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

Introduction

The paper "LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence" introduces a sophisticated system utilizing LLMs for identifying competing drugs related to specific indications, a crucial aspect of drug asset evaluation in competitive drug markets. The research addresses the complexities of consolidating disparate data sources — from scientific literature to clinical trial registries — and empowers LLMs to automate this task, effectively reducing the labor and time traditionally required by analysts.

System Overview

The core innovation emphasizes an LLM-based competitor-discovery agent that processes multi-modal and unstructured diligence memos to map indications to competing drugs. The system achieves 83% recall by integrating a competitor validating LLM-as-a-judge agent, which filters out false positives to enhance precision. This approach significantly surpasses baseline performances demonstrated by OpenAI's Deep Research (65%) and Perplexity Labs (60%) models.

The practical efficacy of the system is evident where it reduced analyst turnaround time from 2.5 days to approximately 3 hours in a case paper. This drastic improvement emphasizes the system's utility in expediting decision-making processes in enterprise environments without sacrificing accuracy.

Methodological Developments

Data Transformation and Benchmarking

The authors curated a benchmark dataset from five years of historical diligence memos, transforming them into a structured format suitable for LLM evaluation. The dataset includes:

1. Competitors Dataset: Enumerates competitors per indication.
2. Attributes Dataset: Captures canonical drug attributes.
3. Competitor-Validator Dataset: Enables tuning of precision filters.

Each memo undergoes hierarchical parsing, translating content into structured JSON objects that capture essential competitive landscape information.

Model and Framework Evaluation

The system employs the ReAct framework, a reason-and-act methodology, to integrate reasoning with interaction in LLMs. This architecture outperforms traditional single-step LLM executions, particularly in handling complex multi-hop queries essential for comprehensive competitive landscape mapping (Figure 1).

Figure 1: Model performance across varying levels of sample difficulty. The x-axis denotes difficulty thresholds, allowing assessment of how different agents perform on increasingly difficult samples, using a non-web baseline as the difficulty proxy.

Competitor-Validator Agent

Precision is maximized by an LLM-as-a-judge agent that employs web-grounded search strategies for validation, filtering out irrelevant drugs. This agent excels with a 90.4% precision and an 85.7% recall on test datasets, achieving a reliable F1-score of 88.0%, indicating its substantial effectiveness in maintaining high precision.

Practical Implications

Deployment and Impact

The deployed system integrates into a competitive analysis workflow, substantially optimizing the due diligence process in drug development evaluations. It couples a lightweight front-end interface with a robust back-end supported by graph-oriented agent services. The high efficiency indicates promising scalability for such LLM applications across various sectors in life sciences and beyond.

The empirical success of the system suggests potential future developments could involve more sophisticated model training techniques or expanded datasets to further enhance precision and recall metrics. Continuous improvement and validation against fresh datasets is crucial to maintaining accuracy and competency.

Conclusion

The paper presents a comprehensive framework leveraging LLMs to automate and enhance competitive landscape evaluations in drug asset management. Its effective deployment illustrates a future where such systems could become pivotal in life sciences and other domains requiring intricate data analysis, setting the precedent for adopting AI in domains with complex data environments.