MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
This work addresses a problem in drug discovery and materials science by enabling efficient molecular design with limited data, though it appears incremental as it builds on existing meta-learning and generative approaches.
The paper tackles the challenge of molecular generation in data-scarce scenarios by proposing MetaMolGen, a meta-learning-based model that generates valid and diverse SMILES sequences, outperforming conventional baselines in few-shot and property-conditioned settings.
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.