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arXiv:2608. 13826v1 Announce Type: cross Abstract: Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions.
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.
The paper investigates how explicitly supervising molecular embeddings with a molecule’s Bemis‑Murcko scaffold influences representation learning. Experiments compare Euclidean and Lorentz contrastive objectives under two augmentation strengths, showing that scaffold‑supervised models consistently group molecules by identical and related scaffolds. These embeddings also enhance property prediction on several tasks, though the magnitude of improvement varies with the target property and the geometry used.
arXiv:2604. 27810v2 Announce Type: replace Abstract: Computational molecular representations underpin virtual screening, property prediction, and materials discovery.
arXiv:2608. 10480v1 Announce Type: new Abstract: Large language models (LLMs) are widely applied across chemical tasks, such as molecular property prediction, which underpins drug discovery.
Large language models (LLMs) are widely applied across chemical tasks, such as molecular property prediction, which underpins drug discovery. Molecular LLMs represent a molecule through several modalities, notably a 1D SMILES sequence or a 2D molecular graph.