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

TL;DR

Protect$^*$ is a neuro-symbolic framework that enhances LLM-based retrosynthesis by integrating chemical logic and expert constraints, enabling more reliable and controllable synthetic pathway generation.

Contribution

It introduces a hybrid neuro-symbolic system combining rule-based reasoning with neural models for chemically guided retrosynthesis.

Findings

Successfully applied to complex natural products

Enabled discovery of a novel Erythromycin B pathway

Improved control and reliability in retrosynthesis generation

Abstract

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.