Generating molecules that satisfy precise numeric constraints over multiple physicochemical properties is critical and challenging. Although large language models (LLMs) are expressive, they struggle with precise multi-objective control and numeric reasoning without external structure and feedback. We introduce \textbf{M olGen}, a fragment-level, retrieval-augmented, two-stage framework for molecule generation under multi-property constraints. Stage I : Prototype generation: a multi-agent reasoner performs retrieval-anchored, fragment-level edits to produce a candidate near the feasible region. Stage II : RL-based fine-grained optimization: a fragment-level optimizer trained with Group Relative Policy Optimization (GRPO) applies one- or multi-hop refinements to explicitly minimize the property errors toward our target while regulating edit complexity and deviation from the prototype. A large, automatically curated dataset with reasoning chains of fragment edits and measured property deltas underpins both stages, enabling deterministic, reproducible supervision and controllable multi-hop reasoning. Unlike prior work, our framework better reasons about molecules by leveraging fragments and supports controllable refinement toward numeric targets. Experiments on generation under two sets of property constraints (QED, LogP, Molecular Weight and HOMO, LUMO) show consistent gains in validity and precise satisfaction of multi-property targets, outperforming strong LLMs and graph-based algorithms.

翻译：生成满足多种理化性质精确数值约束的分子至关重要且极具挑战性。尽管大语言模型（LLMs）具有强大的表达能力，但在缺乏外部结构和反馈的情况下，它们难以实现精确的多目标控制和数值推理。我们提出了 \textbf{M olGen}，一种基于片段、检索增强的两阶段框架，用于多属性约束下的分子生成。第一阶段：原型生成：一个多智能体推理器执行基于检索的片段级编辑，生成接近可行区域的候选分子。第二阶段：基于强化学习的细粒度优化：一个采用组相对策略优化（GRPO）训练的片段级优化器，通过单跳或多跳优化，在调控编辑复杂度和原型偏离度的同时，显式最小化属性误差以逼近目标值。一个大规模自动构建的数据集（包含片段编辑推理链及测量的属性变化量）为两个阶段提供了支撑，实现了确定、可复现的监督以及可控的多跳推理。与先前工作不同，我们的框架通过利用片段更好地对分子进行推理，并支持面向数值目标的可控优化。在两组属性约束（QED、LogP、分子量以及HOMO、LUMO）下的生成实验表明，该方法在分子有效性和多属性目标的精确满足度上均取得了一致的提升，优于强大的LLMs和基于图的算法。