A Novel AI Approach Utilizing Large Language Models and GWAS to Validate Drug Target Efficacy and Predict Adverse Effects
Year: 2026
Type: article
Abstract: The early-stage failure of drug candidates remains a significant challenge in pharmaceutical development, primarily driven by inadequate validation of drug targets and unforeseen adverse drug reactions. This study introduces a novel framework that integrates Large Language Model (LLM)-driven reasoning with Genome-Wide Association Study (GWAS) data to improve drug target validation and predict adverse effects in the early phases of drug development. The system was benchmarked using an integrated multimodal dataset curated from the GWAS Catalog, UniProtKB, DrugBank, ChEMBL, and PubMed literature. To ensure rigorous performance assessment, we employed a validation strategy using controlled data splits and time-aware temporal filtering, which restricts retrieval to evidence available prior to a chosen cutoff to simulate realistic early-stage decision-making. Performance was evaluated against several baseline methods, including GWAS-only evidence scoring, LLM reasoning without retrieval grounding, and single-source analysis. Evaluations revealed that the integrated framework significantly outperformed these baselines, achieving an Area Under the Curve (AUC) of 0.91 in target validation and improving adverse effect prediction accuracy by 24%. Specifically, the framework detected 58% of potential adverse effects during Phase I/preclinical stages, substantially higher than the 23% detected by traditional methods. These results highlight the potential of AI-enhanced drug development processes to reduce costs, accelerate timelines, and improve the early-stage de-risking of therapeutic targets.
Author: Qingyi Zhu
Institution: Shanghai American School
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7164362442
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