{"total":87,"rows":[{"id":"2106.09553","title":"Large-Scale Chemical Language Representations Capture Molecular Structure and Properties","created":"2021-06-17","cited_by_count":206,"area":null},{"id":"2312.02783","title":"Large Language Models on Graphs: A Comprehensive Survey","created":"2023-12-05","cited_by_count":192,"area":null},{"id":"2208.09481","title":"Graph neural networks for materials science and chemistry","created":"2022-08-05","cited_by_count":182,"area":null},{"id":"2110.07814","title":"Meta-learning via Language Model In-context Tuning","created":"2021-10-15","cited_by_count":164,"area":null},{"id":"2206.11140","title":"Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries","created":"2022-06-22","cited_by_count":112,"area":null},{"id":"2102.07916","title":"Few-Shot Graph Learning for Molecular Property Prediction","created":"2021-02-16","cited_by_count":81,"area":null},{"id":"2304.05055","title":"A Comprehensive Survey on Deep Graph Representation Learning","created":"2023-04-11","cited_by_count":72,"area":null},{"id":"2203.14500","title":"MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design","created":"2022-03-28","cited_by_count":66,"area":null},{"id":"2303.03363","title":"Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language","created":"2023-03-06","cited_by_count":44,"area":null},{"id":"2107.07994","title":"Property-Aware Relation Networks for Few-Shot Molecular Property Prediction","created":"2021-07-16","cited_by_count":41,"area":null},{"id":"2407.14962","title":"Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives","created":"2024-07-20","cited_by_count":30,"area":null},{"id":"2106.05386","title":"Artificial Intelligence in Drug Discovery: Applications and Techniques","created":"2021-06-09","cited_by_count":28,"area":null},{"id":"2203.08031","title":"Data-Efficient Graph Grammar Learning for Molecular Generation","created":"2022-03-15","cited_by_count":26,"area":null},{"id":"2305.09481","title":"Context-enriched molecule representations improve few-shot drug discovery","created":"2023-04-24","cited_by_count":24,"area":null},{"id":"2104.15079","title":"Ranking the information content of distance measures","created":"2021-04-30","cited_by_count":22,"area":null},{"id":"2205.02708","title":"Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction","created":"2022-05-05","cited_by_count":21,"area":null},{"id":"2209.05582","title":"Graph Neural Networks for Molecules","created":"2022-09-12","cited_by_count":21,"area":null},{"id":"2210.12839","title":"Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity","created":"2022-10-23","cited_by_count":21,"area":null},{"id":"2406.15523","title":"Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark","created":"2024-06-21","cited_by_count":18,"area":null},{"id":"2310.03030","title":"GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction","created":"2023-09-20","cited_by_count":14,"area":null},{"id":"2205.13457","title":"AutoTSG: Learning and Synthesis for Incident Troubleshooting","created":"2022-05-26","cited_by_count":12,"area":null},{"id":"2106.06410","title":"What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data","created":"2021-06-11","cited_by_count":10,"area":null},{"id":"2110.02667","title":"Attentive Walk-Aggregating Graph Neural Networks","created":"2021-10-06","cited_by_count":10,"area":null},{"id":"2206.08262","title":"Attention-wise masked graph contrastive learning for predicting molecular property","created":"2022-05-02","cited_by_count":10,"area":null},{"id":"2212.01574","title":"Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS","created":"2022-12-03","cited_by_count":10,"area":null},{"id":"2303.00313","title":"Deep Learning Methods for Small Molecule Drug Discovery: A Survey","created":"2023-03-01","cited_by_count":10,"area":null},{"id":"2306.16780","title":"Graph Sampling-based Meta-Learning for Molecular Property Prediction","created":"2023-06-29","cited_by_count":10,"area":null},{"id":"2209.08907","title":"Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning","created":"2022-09-19","cited_by_count":9,"area":null},{"id":"2408.14811","title":"Brain-inspired Artificial Intelligence: A Comprehensive Review","created":"2024-08-27","cited_by_count":9,"area":null},{"id":"2411.16567","title":"Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches","created":"2024-11-25","cited_by_count":9,"area":null},{"id":"2305.09790","title":"Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation","created":"2023-04-27","cited_by_count":8,"area":null},{"id":"2108.05010","title":"Prototype Completion for Few-Shot Learning","created":"2021-08-11","cited_by_count":7,"area":null},{"id":"2109.14772","title":"An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics","created":"2021-09-30","cited_by_count":7,"area":null},{"id":"2103.09656","title":"Set-to-Sequence Methods in Machine Learning: a Review","created":"2021-03-17","cited_by_count":6,"area":null},{"id":"2301.05860","title":"State of the Art and Potentialities of Graph-level Learning","created":"2023-01-14","cited_by_count":6,"area":null},{"id":"2604.16123","title":"Tabular foundation models for in-context prediction of molecular properties","created":"2026-04-17","cited_by_count":6,"area":null},{"id":"2310.00614","title":"PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction","created":"2023-10-01","cited_by_count":4,"area":null},{"id":"2411.01158","title":"Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction","created":"2024-11-02","cited_by_count":4,"area":null},{"id":"2502.09860","title":"Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design","created":"2025-02-14","cited_by_count":4,"area":null},{"id":"2111.04993","title":"Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition","created":"2021-11-09","cited_by_count":3,"area":null},{"id":"2202.01940","title":"Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks","created":"2022-02-04","cited_by_count":3,"area":null},{"id":"2209.01435","title":"A comprehensive survey on recent deep learning-based methods applied to surgical data","created":"2022-09-03","cited_by_count":3,"area":null},{"id":"2304.10510","title":"Censoring chemical data to mitigate dual use risk","created":"2023-04-20","cited_by_count":3,"area":null},{"id":"2311.07126","title":"How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective","created":"2023-11-13","cited_by_count":3,"area":null},{"id":"2401.04478","title":"TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction","created":"2024-01-09","cited_by_count":3,"area":null},{"id":"2405.02845","title":"Data-Efficient Molecular Generation with Hierarchical Textual Inversion","created":"2024-05-05","cited_by_count":3,"area":null},{"id":"2410.05975","title":"Learning to Learn with Contrastive Meta-Objective","created":"2024-10-08","cited_by_count":3,"area":null},{"id":"2104.07255","title":"Embedding Adaptation is Still Needed for Few-Shot Learning","created":"2021-04-15","cited_by_count":2,"area":null},{"id":"2203.01482","title":"MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning","created":"2022-03-03","cited_by_count":2,"area":null},{"id":"2210.05572","title":"Knowledge-Driven New Drug Recommendation","created":"2022-10-11","cited_by_count":2,"area":null},{"id":"2211.04088","title":"A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming","created":"2022-11-08","cited_by_count":2,"area":null},{"id":"2308.09086","title":"Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions","created":"2023-08-17","cited_by_count":2,"area":null},{"id":"2312.15086","title":"HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings","created":"2023-12-22","cited_by_count":2,"area":null},{"id":"2404.08657","title":"Advancing Extrapolative Predictions of Material Properties through Learning to Learn","created":"2024-03-25","cited_by_count":2,"area":null},{"id":"2405.15544","title":"Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction","created":"2024-05-24","cited_by_count":2,"area":null},{"id":"2506.11877","title":"Robust Molecular Property Prediction via Densifying Scarce Labeled Data","created":"2025-06-13","cited_by_count":2,"area":null},{"id":"2107.08987","title":"Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder -- Insights for practical AI-driven molecule generation","created":"2021-07-19","cited_by_count":1,"area":null},{"id":"2110.05478","title":"An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design","created":"2021-10-10","cited_by_count":1,"area":null},{"id":"2111.07439","title":"Improving Compound Activity Classification via Deep Transfer and Representation Learning","created":"2021-11-14","cited_by_count":1,"area":null},{"id":"2312.13486","title":"Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror Descent","created":"2023-12-20","cited_by_count":1,"area":null},{"id":"2403.00865","title":"Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning","created":"2024-03-01","cited_by_count":1,"area":null},{"id":"2410.19704","title":"Multi-view biomedical foundation models for molecule-target and property prediction","created":"2024-10-25","cited_by_count":1,"area":null},{"id":"2509.13527","title":"Meta-Learning Linear Models for Molecular Property Prediction","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2601.11135","title":"Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction","created":"2026-01-16","cited_by_count":1,"area":null},{"id":"2602.11618","title":"How Well Do Large-Scale Chemical Language Models Transfer to Downstream Tasks?","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2201.09202","title":"One-Shot Learning on Attributed Sequences","created":"2022-01-23","cited_by_count":0,"area":null},{"id":"2202.07096","title":"Learning to Discover Medicines","created":"2022-02-14","cited_by_count":0,"area":null},{"id":"2204.00929","title":"AutoProtoNet: Interpretability for Prototypical Networks","created":"2022-04-02","cited_by_count":0,"area":null},{"id":"2302.02055","title":"Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction","created":"2023-02-04","cited_by_count":0,"area":null},{"id":"2303.06902","title":"Molecular Property Prediction by Semantic-invariant Contrastive Learning","created":"2023-03-13","cited_by_count":0,"area":null},{"id":"2305.17817","title":"Transfer Learning for Power Outage Detection Task with Limited Training Data","created":"2023-05-28","cited_by_count":0,"area":null},{"id":"2306.05291","title":"One shot learning based drivers head movement identification using a millimetre wave radar sensor","created":"2023-05-31","cited_by_count":0,"area":null},{"id":"2309.01788","title":"Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction","created":"2023-09-04","cited_by_count":0,"area":null},{"id":"2311.16328","title":"Target-Free Compound Activity Prediction via Few-Shot Learning","created":"2023-11-27","cited_by_count":0,"area":null},{"id":"2402.00092","title":"Episodic-free Task Selection for Few-shot Learning","created":"2024-01-31","cited_by_count":0,"area":null},{"id":"2404.02314","title":"A Strong Baseline for Molecular Few-Shot Learning","created":"2024-04-02","cited_by_count":0,"area":null},{"id":"2407.01886","title":"Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning","created":"2024-07-02","cited_by_count":0,"area":null},{"id":"2410.20711","title":"Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery","created":"2024-10-28","cited_by_count":0,"area":null},{"id":"2503.09049","title":"Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2505.11878","title":"AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery","created":"2025-05-17","cited_by_count":0,"area":null},{"id":"2506.08618","title":"HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystals","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2509.02418","title":"Learnable Loss Geometries with Mirror Descent for Scalable and Convergent Meta-Learning","created":"2025-09-02","cited_by_count":0,"area":null},{"id":"2510.21088","title":"M-GLC: Motif-Driven Global-Local Context Graphs for Few-shot Molecular Property Prediction","created":"2025-10-24","cited_by_count":0,"area":null},{"id":"2511.13758","title":"ChemFixer: Correcting Invalid Molecules to Unlock Previously Unseen Chemical Space","created":"2025-11-14","cited_by_count":0,"area":null},{"id":"2602.20573","title":"MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2606.08221","title":"De novo molecular generation with optical property preconditioning at the token level","created":"2026-06-06","cited_by_count":0,"area":null},{"id":"2609.33342","title":"CalibHyper: Chance-Corrected Relational Hypergraphs for Few-Shot Molecular Property Prediction","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}