Generative AI for Pharmaceutical Literature Mining

Abstract

The rapid expansion of pharmaceutical publications has made conventional keyword-based literature review increasingly difficult to scale, reproduce, and update. Generative artificial intelligence offers new capabilities for interpreting scientific language, connecting dispersed evidence, extracting drug-related relationships, and producing structured knowledge summaries. However, existing generative systems often optimize linguistic fluency without adequately verifying whether generated claims are supported by authentic, temporally relevant, and contextually consistent sources. This study identifies a research gap in the absence of an integrated literaturemining framework that jointly evaluates evidence grounding, citation fidelity, contradiction risk, and knowledge freshness. To address this gap, the study proposes a temporally aware, evidence-traceable generative literature-mining architecture for pharmaceutical research. The proposed approach combines hybrid document retrieval, biomedical entity normalization, claim-level evidence alignment, contradiction detection, and uncertainty-sensitive text generation. The framework is designed to support drug discovery, safety surveillance, therapeutic comparison, formulation research, and regulatory intelligence without treating generated narratives as substitutes for expert judgement. The anticipated contribution is a reproducible and auditable method for transforming fragmented pharmaceutical literature into structured evidence while reducing unsupported synthesis and citation-related