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Molecular generation is a core task in drug discovery. Although existing deep generative models can produce valid molecular structures, they lack precise control over molecular properties such as lipophilicity (logP) and drug-likeness (QED). This paper proposes Property-Regularized Graph Variational Autoencoder (PR-GVAE), a conditional graph VAE that introduces Feature-wise Linear Modulation (FiLM) conditioning in the encoder and a dual-branch fusion architecture in the decoder for precise property control. Experiments on a filtered subset of ZINC-20 (approximately 52,000 molecules, at most 20 heavy atoms) across five models spanning graph-based, sequence-based, and fragment-assembly paradigms demonstrate that PRGVAE achieves a Condition Satisfaction Rate (CSR) for both properties of 78.3%, outperforming the unconditional Vanilla GraphVAE by 2.0× (39.4%) and the sequence-based SMILES C-VAE by 40 percentage points (38.3%). Ablation experiments reveal that decoder conditioning is the core driver of property control (removing it reduces CSR-Both by 26.6%), while encoder FiLM conditioning and dual-branch fusion exhibit additive contributions (simultaneous removal reduces CSR-Both by 30.8%). Visualization confirms that PR-GVAE generates structurally diverse drug-like molecules, providing an effective solution for on-demand molecular design.
