# Protect^*: Steerable Retrosynthesis through Neuro-Symbolic State Encoding

arXiv: https://arxiv.org/abs/2602.13419 · HTML: https://mlsanity.com/paper/2602.13419/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models](https://mlsanity.com/paper/2406.11831/) (2024)
- [DeepRetro: Retrosynthetic Pathway Discovery using Iterative LLM Reasoning](https://mlsanity.com/paper/2507.07060/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have shown remarkable potential in scientific domains like retrosynthesis; yet, they often lack the fine-grained control necessary to navigate complex problem spaces without error. A critical challenge is directing an LLM to avoid specific, chemically sensitive sites on a molecule - a task where unconstrained generation can lead to invalid or undesirable synthetic pathways. In this work, we introduce Protect^*, a neuro-symbolic framework that grounds the generative capabilities of Large Language Models (LLMs) in rigorous chemical logic. Our approach combines automated rule-based reasoning - using a comprehensive database of 55+ SMARTS patterns and 40+ characterized protecting groups - with the generative intuition of neural models. The system operates via a hybrid architecture: an ``automatic mode'' where symbolic logic deterministically identifies and guards reactive sites, and a ``human-in-the-loop mode'' that integrates expert strategic constraints. Through ``active state tracking,'' we inject hard symbolic constraints into the neural inference process via a dedicated protection state linked to canonical atom maps. We demonstrate this neuro-symbolic approach through case studies on complex natural products, including the discovery of a novel synthetic pathway for Erythromycin B, showing that grounding neural generation in symbolic logic enables reliable, expert-level autonomy.