scShapeBench supplies synthetic and real annotated single-cell datasets across four shape categories, with scReebTower outperforming PAGA and Mapper on topology-aware metrics.
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Strategies for pre-training graph neural networks
23 Pith papers cite this work. Polarity classification is still indexing.
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SoftBlobGIN combines ESM-2 representations with protein contact graphs via a lightweight GNN and differentiable substructure pooling to achieve 92.8% accuracy on enzyme classification, raise binding-site AUROC to 0.983, and generate auditable structural explanations without retraining the language模型
GNNs are shown to lack continuity under graph resolution changes due to message-passing schemes, with a derived modification enabling consistent multi-scale representations validated experimentally.
Trained MPNNs factor through bounded step-graphon-signals that embed via an explicit map into disjoint caps on the n-sphere, producing a topological fingerprint for model comparison and retrieval.
AgForce improves antigen-conditioned antibody design by using framework dropout, gated bottlenecks, hyperbolic cross attention, MDN sequence head with Potts-like coupling, annealed MCL, and antigen cycle consistency to achieve 8% better amino acid recovery and superior binding metrics on CHIMERA-BEN
EvoStruct integrates evolutionary priors from a protein language model with structural priors from an E(3)-equivariant GNN to raise amino acid recovery by 16% and diversity by 2.3x on CHIMERA-Bench while cutting perplexity 43%.
MolCHG uses a multi-level compositional hierarchical graph with atom-bond cross-view contrastive learning, functional group prediction, and structure tasks to achieve top results on seven of nine MoleculeNet benchmarks.
SCOPE-BENCH shows state-of-the-art molecular models suffer up to 8x higher errors under extreme OOD, while POMA reduces mean absolute error by up to 11.2% via target-aware source selection and dual-scale adaptation.
A pre-trained interference-aware graph Transformer model for wireless resource allocation that achieves strong few-shot adaptation to new tasks and scenarios.
BiScale-GTR achieves claimed state-of-the-art results on MoleculeNet, PharmaBench and LRGB by combining improved fragment tokenization with a parallel GNN-Transformer architecture that operates at both atom and fragment scales.
MolDA is a multimodal molecular model that uses a discrete large language diffusion backbone plus a hybrid graph encoder to achieve better global coherence and validity than autoregressive approaches.
EnFlow integrates flow-based conformer generation with energy landscape modeling to enable joint ensemble generation and ground-state identification using only 1-2 ODE steps.
SSL4RL reformulates self-supervised learning objectives into dense, verifiable reward signals for RL-based fine-tuning of vision-language models, yielding performance gains on reasoning benchmarks.
GraphPINE is a GNN architecture that initializes node importance from prior knowledge graphs and propagates updates via an importance propagation layer for interpretable drug response prediction on over 5,000 genes and 952 drugs.
NaFM is a pretrained foundation model for natural products using scaffold-focused contrastive learning and masked graph objectives that achieves SOTA on taxonomy classification, gene/microbial analysis, and virtual screening tasks.
FARM adds atomic-level functional group annotations to create FG-enhanced SMILES and FG graphs, trains them with masked language modeling and GNNs plus contrastive alignment, and reports state-of-the-art results on 8 of 13 MoleculeNet tasks.
A unified training framework for mesh-based ML surrogates in CFD improves accuracy and long-horizon stability by enforcing spatial derivative consistency via multi-node prediction, using temporal cross-attention correction, and adding 3D rotary positional embeddings.
Hyformer jointly models molecule generation and property prediction via alternating attention and joint pre-training, showing synergistic gains in conditional sampling, OOD prediction, and a drug design case for antimicrobial peptides.
DiGGR introduces a self-supervised graph representation learning framework that disentangles latent factors to guide mask modeling and improve representation quality on graph tasks.
Pre-training GNNs on ECFP prediction produces statistically significant QSAR gains on five of six Biogen benchmarks with OOD splits, but underperforms on heterogeneous datasets and complex endpoints like binding affinity.
Neural network and TDA methods outperform PCA at detecting financial anomalies in the Canadian TSX-60 market.
citing papers explorer
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FARM: Enhancing Molecular Representations with Functional Group Awareness
FARM adds atomic-level functional group annotations to create FG-enhanced SMILES and FG graphs, trains them with masked language modeling and GNNs plus contrastive alignment, and reports state-of-the-art results on 8 of 13 MoleculeNet tasks.
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Disentangled Generative Graph Representation Learning
DiGGR introduces a self-supervised graph representation learning framework that disentangles latent factors to guide mask modeling and improve representation quality on graph tasks.
- Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models