🤖 AI Summary
This study addresses the challenge of precisely mitigating specific adverse drug effects while preserving therapeutic efficacy by introducing PRECEDE, a novel framework that incorporates precedent-guided, auditable, and falsifiable reasoning into AI-assisted drug redesign for the first time. PRECEDE employs a large language model as an orchestrator, integrating drug–side effect association data, biomedical knowledge graphs, and historical precedents of safety optimization, while embedding human-in-the-loop review to ensure generated proposals remain within established pharmacological boundaries. Experimental results demonstrate that PRECEDE produces interpretable and traceable drug redesign strategies that effectively balance the retention of therapeutic activity with the alleviation of targeted side effects.
📝 Abstract
We propose PRECEDE, a precedent-guided co-scientist for side-effect-aware drug redesign that revises a parent compound to mitigate a specified side effect while preserving therapeutic function. Rather than isolated molecular generation, PRECEDE frames redesign as evidence-grounded reasoning over drug--side-effect associations, biomedical knowledge graphs, and precedents of safety-driven optimization, coordinated by an LLM orchestrator with explicit policies and human-review checkpoints. We position PRECEDE as a human-supervised AI-for-science workflow in which hypotheses remain auditable, falsifiable, and bounded by prior pharmacology.