A Precedent-Guided Co-Scientist for Side-Effect-Aware Drug Redesign

July 03, 2026 ยท Grace Period ยท ๐Ÿ› the ICML 2026 Workshop on AI for Science

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Authors Yujin Kim, Charmgil Hong arXiv ID 2607.02944 Category cs.LG: Machine Learning Cross-listed cs.AI Citations 0 Venue the ICML 2026 Workshop on AI for Science
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.
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