VQ-SAD: Vector Quantized Structure Aware Diffusion For Molecule Generation
arXiv:2604.08575v1 Announce Type: cross Abstract: Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically
arXiv:2604.21937v1 Announce Type: new Abstract: Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires
arXiv:2609.13519v1 Announce Type: new Abstract: Molecular design is most effective when generation mirrors the edits chemists actually make: extending a scaffold, replacing a
arXiv:2603.18256v1 Announce Type: cross Abstract: Recent advances in reasoning-based large language models (LLMs) have demonstrated substantial improvements in complex
arXiv:2610.04056v1 Announce Type: new Abstract: Synthesizing realistic graphs at scale is vital when the graphs of interest are large and real-world samples are limited or
arXiv:2604.27636v1 Announce Type: new Abstract: Predicting stable and metastable structures is central to molecular and materials discovery, but remains limited by the cost of
arXiv:2605.20235v1 Announce Type: cross Abstract: Diffusion models generate high-dimensional data with remarkable quality, yet how their training efficiently learns the score
arXiv:2603.22294v1 Announce Type: cross Abstract: Synthetic Data Generation (SDG), leveraging Large Language Models (LLMs), has recently been recognized and broadly adopted as an
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