Personalized Drug Discovery Knowledge Retrieval Using Large Language Models and Biomedical Context Representation
Keywords:
personalized retrieval; large language models; drug discovery; biomedical knowledge graphs; context representation; retrieval-augmented generation; fairnessAbstract
The accelerating growth of biomedical literature and multi-modal data has created an urgent need for intelligent retrieval systems that can navigate complex pharmacological spaces while adapting to the unique research contexts of individual scientists. This paper presents a system-level examination of personalized drug discovery knowledge retrieval that leverages large language models and rich biomedical context representations. Unlike generic search engines, the envisioned architecture fuses unstructured scientific text with structured knowledge graphs, molecular embeddings, and ontological frameworks to ground large language model reasoning in verifiable biomedical evidence. We analyze the structural trade-offs inherent in coupling retrieval-augmented generation pipelines with user profiling, focusing on how semantic context beyond surface-level keywords can be constructed from entity-linked biomedical concepts and relation-aware transformer encodings. The discussion extends to deployment-scale considerations, including latency-aware model serving, sustainability constraints of large-scale inference, and the maintenance of dynamic knowledge indices in the face of rapidly evolving biological databases. Furthermore, the paper addresses governance challenges related to algorithmic fairness in retrieved drug candidates, potential biases against underrepresented disease populations, and the explainability requirements for high-stakes pharmaceutical decision support. Policy implications are examined through the lens of regulatory oversight, intellectual property around AI-suggested compounds, and the need for auditable, transparent retrieval trajectories. By synthesizing perspectives from information retrieval, biomedical informatics, and socio-technical systems research, we offer a forward-looking framework that balances personalization depth with ethical and infrastructural robustness, charting a path toward responsible, context-aware knowledge discovery in drug development.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
