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Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining
arXiv:2509.22468v2 Announce Type: replace Abstract: High-quality molecular representations are essential for property prediction and molecular design, yet large labeled datasets remain scarce. While self-supervised pretraining on molecular graphs has shown promise, many existing approaches either depend on hand-crafted augmentations or complex generative objectives, and often rely solely on 2D topology, leaving valuable 3D structural information underutilized. To address this gap, we...