A Precedent-Guided Co-Scientist for Side-Effect-Aware Drug Redesign
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Computer Science > Machine Learning
Title:A Precedent-Guided Co-Scientist for Side-Effect-Aware Drug Redesign
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.
| Comments: | Accepted at the ICML 2026 Workshop on AI for Science |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.02944 [cs.LG] |
| (or arXiv:2607.02944v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.02944
arXiv-issued DOI via DataCite (pending registration)
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