My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents

• URL: http://arxiv.org/abs/2606.11256v1
• Date: Mon, 08 Jun 2026 23:52:52 GMT
• Title: My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents
• Abstract summary: My Chemical Harness is a route-native evolutionary framework for goal-directed molecular design.<n>Each route is built from purchasable building blocks and reaction templates, executed by deterministic chemistry tools, and scored through task-specific molecular oracles.<n>Large language models (LLMs) are used only as strategy controllers that select high-level preferences over route length, move type, reaction families, motifs, and exploration pressure.
• Score: 2.0798270860482964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes. We introduce My Chemical Harness, a route-native evolutionary framework for goal-directed molecular design in which the search population consists of executable synthetic pathways rather than isolated molecular graphs. Each route is built from purchasable building blocks and reaction templates, executed by deterministic chemistry tools, and scored through task-specific molecular oracles. Large language models (LLMs) are used only as strategy controllers that select high-level preferences over route length, move type, reaction families, motifs, and exploration pressure, while local code performs route construction, validation, deduplication, scoring, selection, and memory updates. This separation lets the LLM guide exploration without allowing it to introduce hallucinated products or unsupported reaction steps. On a soluble epoxide hydrolase proxy task, our LLM agent improves over single pass LLM and deterministic controllers, reaching state-of-the-art performance across the sEH score, synthetic accessibility score, and AiZynthFinder success rate metrics. These results suggest that constrained LLM agents can play a significant role in molecular discovery without requiring training, fine-tuning, or dedicated generative models.

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