[2508.16571] LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence

Computer Science > Artificial Intelligence arXiv:2508.16571 (cs) [Submitted on 22 Aug 2025 (v1), last revised 8 May 2026 (this version, v4)] Title:LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence Authors:Vlad Vinogradov (1), Alisa Vinogradova (2), Dmitrii Radkevich (1), Ilya Yasny (1), Dmitry Kobyzev (1), Ivan Izmailov (1), Katsiaryna Yanchanka (1), Roman Doronin (1), Andrey Doronichev (1) ((1) Optic Inc., (2) AI Expert) View a PDF of the paper titled LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence, by Vlad Vinogradov (1) and 9 other authors View PDF HTML (experimental) 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 di...