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 generativethis http URLresults 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.
View on arXiv@article{yan2025_2504.15587,
title={ MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design },
author={ Zimo Yan and Jie Zhang and Zheng Xie and Chang Liu and Yizhen Liu and Yiping Song },
journal={arXiv preprint arXiv:2504.15587},
year={ 2025 }
}