A graph-grammar cheminformatics workflow expands the carbon fixation reaction network, then uses integer linear programming flow queries to surface short autocatalytic pathways producing Acetyl-CoA and Malate with efficiencies approaching the CETCH cycle.
DGCNN introduces the EdgeConv operator, which constructs k-nearest neighbor graphs dynamically in feature space at each network layer. This enables the model to capture both local geometry and long-range semantic relationships for point cloud classification and segmentation.
An updated description of nauty and introduction of Traces, two programs for graph isomorphism testing and canonical labeling using the individualization-refinement paradigm.
Surge is a constitutional isomer generator based on the canonical generation path method, using nauty for graph automorphism computation. Its three-stage pipeline (simple graph generation, vertex coloring for atom assignment, edge multiplicity for bond orders) generates 7-22 million molecules per second, outperforming MOLGEN 5.0 by 42-161x on natural product molecular formulas.
Proposes Ewald message passing, a Fourier-space scheme inspired by Ewald summation that captures long-range interactions in molecular graphs. The method is architecture-agnostic and improves energy MAEs by 10% on OC20 and 16% on OE62 across four baseline GNN models.
Lagrangian Neural Networks (LNNs) use neural networks to parameterize arbitrary Lagrangians, enabling energy-conserving learned dynamics without canonical coordinates. Unlike Hamiltonian approaches, LNNs handle relativistic systems and extend to graphs via Lagrangian Graph Networks.
DrugChat is a prototype system that bridges molecular graph neural networks with large language models for interactive, multi-turn question answering about drug compounds. It trains only a lightweight linear adaptor between a frozen GNN encoder and Vicuna-13B using 143K curated QA pairs from ChEMBL and PubChem.
MolFM pre-trains a multimodal encoder that fuses 2D molecular graphs, biomedical text, and knowledge graph entities through fine-grained cross-modal attention, achieving strong gains on cross-modal retrieval, molecule captioning, text-based generation, and property prediction.
MoMu pre-trains dual graph and text encoders on 15K molecule graph-text pairs using contrastive learning, enabling cross-modal retrieval, molecule captioning, zero-shot text-to-graph generation, and improved molecular property prediction.
Frey et al. discover empirical power-law scaling relations for both chemical language models (ChemGPT, up to 1B parameters) and equivariant GNN interatomic potentials, finding that neither domain has saturated with respect to model size, data, or compute.
GraSP introduces a general framework for recognizing graphs in images by framing it as sequential subgraph prediction with a binary classifier. A GNN conditions a CNN via FiLM layers to predict whether a candidate graph is a subgraph of the target. Applied to OCSR on QM9, GraSP achieves 67.5% accuracy with no domain-specific modifications.
Battaglia et al. argue that combinatorial generalization requires structured representations, systematically analyze the relational inductive biases in standard deep learning architectures (MLPs, CNNs, RNNs), and present the graph network as a unifying framework that generalizes and extends prior graph neural network approaches.