MolMiner可精准控制分子多属性，生成三维结构更优的化合物。
MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design
- 基于片段的无序生成，结合对称性和三维几何约束。
- 在指定性质范围内，命中率提升最高达5.25倍，超越训练分布。
- 适合需要多属性协同优化的药物分子设计者使用。
我们提出MolMiner，一种基于片段、具备几何感知能力且对生成顺序不敏感的自回归分子设计模型。该模型支持从部分结构出发，对十二种理化与结构属性进行高维条件控制；通过考虑对称性的片段连接方式构建分子，并在每一步生成中基于力场松弛后的三维结构进行条件化。条件控制无需额外属性损失函数即可自然涌现。在目标性质窗口内，条件生成的命中率最高可达无条件生成的5.25倍，以及训练分布本身的3.5倍，有效克服模型固有偏见，仅伴随轻微的无条件分布保真度下降。MolMiner在一个统一框架中整合了动态几何建模、对称性处理、无序生成与可扩展的多属性条件控制。
原文摘要 · Abstract (English)
We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design. MolMiner supports high-dimensional conditional control over twelve physicochemical and structural properties from partial specifications, constructs molecules via symmetry-aware fragment attachments, and conditions each generation step on force-field-relaxed three-dimensional geometry of the partial structure. Conditional control emerges without auxiliary property losses. On targeted property windows, conditioning lifts hit rates by up to 5.25x over unconditional generation and 3.5x over the training distribution itself -- overriding the model's intrinsic biases -- at the cost of a small reduction in unconditional distributional fidelity. MolMiner unifies dynamic geometry, symmetry handling, order-agnostic generation, and scalable multi-property conditioning within a single framework.
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