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Large Language Model-Driven Prioritization of Alzheimer’s Disease Drug Targets Across Multidimensional Criteria
Adaszewski S, Schindler T.. medRxiv, 2025-12-29
Topic: Drug trials: efficacy and safety
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. Graphical Abstract
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APA 7th
Adaszewski, S., & Schindler, T. (2025). Large Language Model-Driven Prioritization of Alzheimer’s Disease Drug Targets Across Multidimensional Criteria. medRxiv. https://doi.org/10.64898/2025.12.28.25343106
Vancouver
Adaszewski S, Schindler T. Large Language Model-Driven Prioritization of Alzheimer’s Disease Drug Targets Across Multidimensional Criteria. medRxiv. 2025. doi:10.64898/2025.12.28.25343106.
BibTeX
@article{adaszewski2025-8771179b,
author = {Adaszewski, S. and Schindler, T.},
title = {{Large Language Model-Driven Prioritization of Alzheimer’s Disease Drug Targets Across Multidimensional Criteria}},
journal = {medRxiv},
year = {2025},
doi = {10.64898/2025.12.28.25343106},
url = {https://doi.org/10.64898/2025.12.28.25343106}
}
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Key findings
- 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.
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Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 19 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 12 of the 62 points we could judge it on, which is 19 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 12 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 12 of the 62 points we could judge it on is 19 out of 100. |
| Total | 19 / 100 | Capped to 16 because this is not a peer-reviewed article. |
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