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arXiv · 2504.15587

MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design

Zimo Yan·Jie Zhang·Zheng Xie·Chang Liu·Yizhen Liu·Yiping Song
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Abstract

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.

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Zimo Yan, Jie Zhang, Zheng Xie, Chang Liu, Yizhen Liu, Yiping Song. 2025-04-22. MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design. https://doi.org/10.46793/match.97-2.11226

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