Benchmarking graph-based models for in-silico toxicity prediction in drug discovery
cs.LG, q-bio.BM, q-bio.QM
Submitted: 2026-09-29
Updated: 2026-09-29
Terminology
Sources
- Self-Supervised Graph Transformer on Large-Scale Molecular Data
- Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure
- Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction
- MoleculeNet: A Benchmark for Molecular Machine Learning
- Mamba: Linear-Time Sequence Modeling with Selective State Spaces
- ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
- Inductive Representation Learning on Large Graphs
- A Generalization of Transformer Networks to Graphs
- Variational Graph Auto-Encoders
- How Powerful are Graph Neural Networks?
- Rethinking pooling in graph neural networks
- Graph Pooling with Node Proximity for Hierarchical Representation Learning
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks