用元学习生成分子图谱，少数据下快速设计目标分子
MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
- 基于元学习构建分子图谱生成模型，映射到标准化潜在空间
- 低数据条件下生成有效且多样的SMILES序列，性能超越基线
- 适合小样本药物与材料分子设计，支持属性条件生成
分子生成在药物发现和材料科学中至关重要，尤其在数据稀缺场景下，传统生成模型常难以实现理想的条件泛化。为此，我们提出MetaMolGen，一种基于一阶元学习的分子生成器，专为少样本和属性条件下的分子生成设计。MetaMolGen通过将分子图谱映射到标准化潜在空间来统一其分布，并采用轻量级自回归序列模型生成忠实反映分子结构的SMILES序列。同时，通过集成可学习的属性投影器，支持目标属性条件下的分子生成。实验表明，MetaMolGen在低数据条件下持续生成有效且多样化的SMILES序列，优于常规基线，展现出快速适应和高效条件生成的优势，适用于实际分子设计。
原文摘要 · Abstract (English)
Molecular generation plays an important role in drug discovery and materials science, especially in data-scarce scenarios where traditional generative models often struggle to achieve satisfactory conditional generalization. To address this challenge, we propose MetaMolGen, a first-order meta-learning-based molecular generator designed for few-shot and property-conditioned molecular generation. MetaMolGen standardizes the distribution of graph motifs by mapping them to a normalized latent space, and employs a lightweight autoregressive sequence model to generate SMILES sequences that faithfully reflect the underlying molecular structure. In addition, it supports conditional generation of molecules with target properties through a learnable property projector integrated into the generative process.Experimental results demonstrate that MetaMolGen consistently generates valid and diverse SMILES sequences under low-data regimes, outperforming conventional baselines. This highlights its advantage in fast adaptation and efficient conditional generation for practical molecular design.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。