The paper investigates how phylogenetic leakage can inflate performance estimates for protein classifiers, particularly in venom ion‑channel target prediction. By treating cross‑species prediction as a structured distribution shift, the authors compare five structural inductive biases using a multimodal graph neural network that fuses ESM‑2 embeddings with a disulfide‑aware attention mechanism. Persistent‑homology representations, which capture backbone geometry through topological descriptors, outperform other biases under a leave‑one‑taxon‑out protocol, achieving 63.4% accuracy versus 54‑55% for other geometric priors and 30% for a sequence‑only baseline.
arXiv:2607. 16553v1 Announce Type: new Abstract: Protein fold classification can be approached via sequence-based representations or structural descriptors, but direct comparisons between lightweight handcrafted descriptors and pretrained protein language model embeddings remain limited.
arXiv:2609.37384v1 Announce Type: new Abstract: Molecular representation learning is central to computer-aided drug discovery. Molecular graphs, SMILES strings, and 3D conformations provide complemen...
The paper presents a fully self‑supervised contrastive learning framework that learns lexical representations from raw IPA‑transcribed wordlists without any cognacy annotations or expert input. Using a dual contrastive objective—word‑level and language‑level losses—the model produces word representations that enable fast computation of pairwise language distances and the inference of a global phylogenetic tree for 3,399 language varieties. The resulting tree achieves a generalized quartet distance competitive with baselines while requiring only minutes of computation on a standard notebook GPU, and the representations also capture diachronic concept stability.
The study investigates whether self‑supervised pretraining improves molecular graph neural networks by adapting the LeJEPA architecture to molecular graphs. While pretraining enhances learned representations and a frozen probe outperforms random initialization on tasks such as ogbg‑molhiv, it does not consistently boost finetuning performance across different data splits. Combining pretrained embeddings with 1024‑bit Morgan fingerprints yields modest gains, indicating that pretraining provides complementary information best exploited at the feature level.
arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.
arXiv:2608. 09099v1 Announce Type: new Abstract: Quantitative estimation of protein-ligand binding affinity from three-dimensional complex structures is a fundamental task in structure-based computational chemistry and molecular modeling.
SMILESGNN is a multimodal architecture that fuses a SMILES Transformer encoder with a GATv2 graph encoder through cross‑attention, enabling interpretable clinical toxicity predictions. The model retains an explicit graph branch, allowing GNNExplainer to identify substructures linked to toxicity. On the ClinTox dataset it achieves an AUC‑ROC of 0.987 and F1 of 0.906 with only 0.4 M parameters, while on Tox21 it attains a mean AUC‑ROC of 0.750, comparable to strong single‑modality baselines.
arXiv:2604. 06336v2 Announce Type: replace-cross Abstract: Fragment-level representations provide a natural way to capture recurring molecular substructures and reuse their learned representations across molecules.
arXiv:2609.35877v1 Announce Type: cross Abstract: Many DNA foundation models are pretrained by masking parts of a sequence and asking the model to reconstruct them. Standard masked pretraining expose...
arXiv:2607. 22458v1 Announce Type: new Abstract: Do learned audio embeddings encode structure that nobody told them to encode?
The paper introduces a fragment‑level graph encoding for molecular property prediction, using BRICS decomposition to represent molecules as graphs of synthetically meaningful fragments. This change of input representation allows any message‑passing GNN and post‑hoc attribution method to produce explanations at the functional‑group level, matching atom‑level accuracy on smaller graphs. Experiments across multiple architectures, tasks, and explainers show that fragment‑level encoding recovers known pharmacophores more reliably than post‑hoc aggregation of atom‑level explanations.
arXiv:2606. 31126v1 Announce Type: new Abstract: Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design.