Suggest-Review-Edit-Verify: Structure-Preserving Edit-Space Reduction for Iterative Refinement in Agentic Molecular Generation

Molecule generation, which aims to discover molecules with desired characteristics, has recently advanced through natural-language instructions that specify diverse design objectives. Recent LLM-based chemistry agents make generation actionable by leveraging these instructions for iterative generation and refinement. Nonetheless, existing methods struggle to identify effective modifications, limiting their efficacy for jointly satisfying structural requirements and multiple property constraints. To address this challenge, we introduce SREV-Mol (Suggest–Review–Edit–Verify) for Molecular Generation), a structure-preserving framework for iterative molecular refinement. Our approach first translates the natural-language structural requirement into SMARTS (SMILES arbitrary target specification) and generates a verified seed molecule, then iteratively selects effective edits from a reduced space of structure-preserving candidates to refine the molecule. To further improve edit selection, we present Structured Edit Reasoning (SER) and candidate-specific Auxiliary Feature Tokens (AFTs), which provide complementary signals for comparing candidate modifications. Thereby, SREV-Mol enables effective multi-property optimization under structural requirements and up to 14 simultaneous property constraints, substantially reducing broad trial-and-error search over the molecule edit space. Extensive experiments on in-distribution (ID) and scaffold out-of-distribution (OOD) settings show that ours outperforms existing chemistry agents and strong frontier LLMs, despite using an 8B backbone, while continuing to improve with larger refinement budgets.