{"total":28,"rows":[{"id":"2305.17589","title":"Graph Inductive Biases in Transformers without Message Passing","created":"2023-05-27","cited_by_count":153,"area":null},{"id":"2402.02216","title":"Position: Graph Foundation Models are Already Here","created":"2024-02-03","cited_by_count":86,"area":null},{"id":"2302.04181","title":"Attending to Graph Transformers","created":"2023-02-08","cited_by_count":83,"area":null},{"id":"2404.11568","title":"On the Scalability of GNNs for Molecular Graphs","created":"2024-04-17","cited_by_count":36,"area":null},{"id":"2306.07812","title":"Automated 3D Pre-Training for Molecular Property Prediction","created":"2023-06-13","cited_by_count":24,"area":null},{"id":"2303.16982","title":"Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+","created":"2023-03-16","cited_by_count":22,"area":null},{"id":"2305.18454","title":"PubChemQC B3LYP/6-31G*//PM6 dataset: the Electronic Structures of 86 Million Molecules using B3LYP/6-31G* calculations","created":"2023-05-29","cited_by_count":12,"area":null},{"id":"2402.04538","title":"Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers","created":"2024-02-07","cited_by_count":12,"area":null},{"id":"2408.10120","title":"Geometry Informed Tokenization of Molecules for Language Model Generation","created":"2024-08-19","cited_by_count":10,"area":null},{"id":"2510.03511","title":"Platonic Transformers: A Solid Choice For Equivariance","created":"2025-10-03","cited_by_count":10,"area":null},{"id":"2407.03953","title":"Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training","created":"2024-07-04","cited_by_count":7,"area":null},{"id":"2506.06290","title":"CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning","created":"2025-05-16","cited_by_count":7,"area":null},{"id":"2305.15747","title":"Union Subgraph Neural Networks","created":"2023-05-25","cited_by_count":6,"area":null},{"id":"2402.07249","title":"Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey","created":"2024-02-11","cited_by_count":6,"area":null},{"id":"2406.16853","title":"GeoMFormer: A General Architecture for Geometric Molecular Representation Learning","created":"2024-06-24","cited_by_count":6,"area":null},{"id":"2304.03724","title":"GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising","created":"2023-03-28","cited_by_count":5,"area":null},{"id":"2511.03981","title":"Structural Priors and Modular Adapters in the Composable Fine-Tuning Algorithm of Large-Scale Models","created":"2025-11-06","cited_by_count":4,"area":null},{"id":"2603.00568","title":"Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions","created":"2026-02-28","cited_by_count":3,"area":null},{"id":"2405.16185","title":"Differentiable Cluster Graph Neural Network","created":"2024-05-25","cited_by_count":2,"area":null},{"id":"2606.11508","title":"Probabilistic Contrastive Pretraining for Multi-task ADME Property Prediction","created":"2026-06-09","cited_by_count":2,"area":null},{"id":"2604.16586","title":"A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era","created":"2026-04-17","cited_by_count":1,"area":null},{"id":"2308.08129","title":"Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?","created":"2023-08-16","cited_by_count":0,"area":null},{"id":"2311.01135","title":"Generating QM1B with PySCF$_{\\text{IPU}}$","created":"2023-11-02","cited_by_count":0,"area":null},{"id":"2409.04909","title":"Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets","created":"2024-09-07","cited_by_count":0,"area":null},{"id":"2412.06847","title":"M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery","created":"2024-12-08","cited_by_count":0,"area":null},{"id":"2602.17071","title":"AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation","created":"2026-02-19","cited_by_count":0,"area":null},{"id":"2602.22405","title":"MolFM-Lite: Multi-Modal Molecular Property Prediction with Conformer Ensemble Attention and Cross-Modal Fusion","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2604.03815","title":"k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS","created":"2026-04-04","cited_by_count":0,"area":null}],"next_cursor":null}