Large Language Model-Driven Prioritization of Alzheimer's Disease Drug Targets Across Multidimensional Criteria

Abstract

Large language models (LLMs) offer new opportunities to synthesize the vast and heterogeneous biomedical literature, yet their potential to support drug target prioritization in complex diseases such as Alzheimer's disease (AD) remains largely unexplored. Here, we introduce an LLM-driven framework that evaluates and ranks AD therapeutic targets across six criteria central to pharmaceutical decision-making: biological confidence, technical feasibility, clinical developability, patient impact, competitive landscape, and safety assessment. Using Gemini 2.5 Pro augmented with real-time web search, we performed large-scale pairwise comparative evaluations and pointwise scoring across a focused set of 522 AD-associated targets with high-quality chemical probes-a tractable subset enriched for clinically advanced targets. We implemented a novel pairwise QuickSort-based ranking procedure that leverages the LLM as a comparative oracle, and benchmarked its performance against pointwise scoring across 16 replicate runs per criterion. Retrieval-augmented LLM reasoning substantially improved early enrichment of clinically validated AD targets, outperforming LLM-only prompting and approaching the performance of the OpenTargets association benchmark. Pairwise comparative reasoning consistently exceeded pointwise scoring across five of six criteria, yielding higher stability, stronger inter-criterion structure, and markedly improved normalized gain metrics. Multi-objective integration using Pareto fronts and utopia-point scoring further enhanced consensus and robustness, producing holistic rankings that nearly matched the strongest individual criteria while exhibiting superior cross-category coherence. Challenges remained in assessing competitiveness and safety-domains with sparse or inconsistent literature representation-highlighting areas where hybrid models integrating structured datasets may be required. Together, these results demonstrate that retrieval-augmented LLMs, when combined with structured comparative prompting and multi-criteria integration, can approximate expert-level reasoning and meaningfully enrich target prioritization pipelines for AD. This framework provides a scalable, interpretable, and biologically grounded approach for early-stage drug discovery, with broad applicability to other complex diseases.

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