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arXiv:2608. 13256v1 Announce Type: cross Abstract: As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical.
EmbeddGAN introduces a new GAN framework that replaces the traditional discriminator with an embedding network trained to maximize statistical dependence between embeddings and real/fake labels using Gini distance correlation (gCor). The generator simultaneously minimizes this dependence, encouraging real and generated samples to become indistinguishable in the learned low‑dimensional embedding space. Experiments on MNIST, CIFAR‑10, and CelebA show competitive performance and notably more stable training dynamics compared to established baselines.
The paper introduces concept bottlenecks into deep graph generators to enhance interpretability and steerability in molecular graph generation. By embedding human-interpretable concepts, the authors demonstrate improved distributional fidelity (lower FCD) while maintaining validity and uniqueness. They also present a Concept Controller optimization method for steering generation, evaluated within a one-shot molecular graph generation framework.
The paper introduces a latent style-based quantum Wasserstein GAN (QGAN) for drug design, integrating a variational autoencoder with a hybrid quantum GAN to generate molecular structures. The QGAN employs style-based noise reuploading and a Wasserstein gradient penalty to enhance trainability and reduce mode collapse, achieving comparable performance to classical models while using far fewer trainable parameters. Experiments on noiseless simulators and an IBM Heron quantum computer demonstrate the model’s capability to sample thousands of new molecules and reveal quantum noise effects.
arXiv:2602. 04119v2 Announce Type: replace Abstract: The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice.
The paper introduces StyleGANCA, a lightweight neural cellular automata (NCA) based generative adversarial network designed for medical image synthesis. By combining a StyleGAN-inspired mapping network with adaptive style modulation in a multi-scale NCA framework, the model achieves high-quality image generation with far fewer parameters than existing adversarial, variational, diffusion, and NCA baselines. Experiments on BloodMNIST and PathMNIST show competitive FID and KID scores, and the synthetic images preserve class-specific information, effectively supporting downstream multi-class classifier training.