MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866918016916127744 |
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| author | Yan, Zimo Zhang, Jie Xie, Zheng Liu, Chang Liu, Yizhen Song, Yiping |
| author_facet | Yan, Zimo Zhang, Jie Xie, Zheng Liu, Chang Liu, Yizhen Song, Yiping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15587 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design Yan, Zimo Zhang, Jie Xie, Zheng Liu, Chang Liu, Yizhen Song, Yiping Machine Learning Artificial Intelligence 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. |
| title | MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.15587 |