Fraglingo: Molecular Design via Attachment-Aware Autoregressive Fragment Generat
arXiv:2603.18256v1 Announce Type: cross Abstract: Recent advances in reasoning-based large language models (LLMs) have demonstrated substantial improvements in complex
arXiv:2605.00354v1 Announce Type: cross Abstract: Many diffusion based molecule generation methods ignore the symbolic information of molecules and represent the atom and bond
arXiv:2604.08575v1 Announce Type: cross Abstract: Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically
arXiv:2607.13115v1 Announce Type: new Abstract: Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often
arXiv:2605.08767v1 Announce Type: new Abstract: Recent advances in generative modeling have enabled significant progress in structure-based drug design (SBDD). Existing methods
arXiv:2603.03686v2 Announce Type: replace Abstract: Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional
arXiv:2605.26543v1 Announce Type: new Abstract: Polymer discovery is central to fields ranging from energy storage to biomedicine, but it is hindered by an astronomically large
arXiv:2608.20755v1 Announce Type: new Abstract: Modern de novo design workflows generate many candidate protein binders, but wet-lab validation capacity remains limited, making
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