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

arXiv: https://arxiv.org/abs/2607.02944 · HTML: https://mlsanity.com/paper/2607.02944/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration](https://mlsanity.com/paper/2411.15692/) (2024)
- [LIDDIA: Language-based Intelligent Drug Discovery Agent](https://mlsanity.com/paper/2502.13959/) (2025)

## Abstract (arXiv)

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.