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

• URL: http://arxiv.org/abs/2504.15587v1
• Date: Tue, 22 Apr 2025 05:04:33 GMT
• Title: MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
• Authors: Zimo Yan, Jie Zhang, Zheng Xie, Chang Liu, Yizhen Liu, Yiping Song,
• Abstract summary: We propose MetaMolGen, a first-order meta-learning-based molecular generator for few-shot and property-conditioned generation.<n>It 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.<n>It supports conditional generation of molecules with target properties through a learnable property projector integrated into the generative process.
• Score: 11.614346021222017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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