Data to 14 Oct 2026

Low Data Drug Discovery with One-shot Learning

arXiv 1611.03199 ↗First posted 10 Nov 2016cs.LG stat.MLcited by 91

Not on the map. Why?

Abstract (arXiv)

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds. However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the residual LSTM embedding, that, when combined with graph convolutional neural networks, significantly improves the ability to learn meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery.

Key facts. Cited by 91 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Nov 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 16; 2022: 20; 2023: 22; 2024: 16; 2025: 10; 2026: 7.

91 papers.

Sort:

By area:

2021Large-Scale Chemical Language Representations Capture Molecular Structure and Propertiescited by 206
2023Large Language Models on Graphs: A Comprehensive Surveycited by 192
2022Graph neural networks for materials science and chemistrycited by 182
2021Meta-learning via Language Model In-context Tuningcited by 164
2022Prompting Decision Transformer for Few-Shot Policy Generalizationcited by 146
2022Understanding and Extending Subgraph GNNs by Rethinking Their Symmetriescited by 112
2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
2023Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Languagecited by 44
2021Property-Aware Relation Networks for Few-Shot Molecular Property Predictioncited by 41
2023MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learningcited by 40

38 more

2024Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectivescited by 30
2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
2023Context-enriched molecule representations improve few-shot drug discoverycited by 24
2021Ranking the information content of distance measurescited by 22
2022Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Predictioncited by 21
2022Graph Neural Networks for Moleculescited by 21
2022Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexitycited by 21
2024Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmarkcited by 18
2023GPT-MolBERTa: GPT Molecular Features Language Model for molecular property predictioncited by 14
2022AutoTSG: Learning and Synthesis for Incident Troubleshootingcited by 12
2021What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Datacited by 10
2021Attentive Walk-Aggregating Graph Neural Networkscited by 10
2022Attention-wise masked graph contrastive learning for predicting molecular propertycited by 10
2022Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUScited by 10
2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
2023Graph Sampling-based Meta-Learning for Molecular Property Predictioncited by 10
2022Learning Symbolic Model-Agnostic Loss Functions via Meta-Learningcited by 9
2024Brain-inspired Artificial Intelligence: A Comprehensive Reviewcited by 9
2024Enhancing Few-Shot Learning with Integrated Data and GAN Model Approachescited by 9
2023Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representationcited by 8
2021Prototype Completion for Few-Shot Learningcited by 7
2021An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analyticscited by 7
2021Set-to-Sequence Methods in Machine Learning: a Reviewcited by 6
2023State of the Art and Potentialities of Graph-level Learningcited by 6
2026Tabular foundation models for in-context prediction of molecular propertiescited by 6
2023PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Predictioncited by 4
2024Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Predictioncited by 4
2025Gradient GA: Gradient Genetic Algorithm for Drug Molecular Designcited by 4
2021Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognitioncited by 3
2022Distribution Embedding Networks for Generalization from a Diverse Set of Classification Taskscited by 3
2022A comprehensive survey on recent deep learning-based methods applied to surgical datacited by 3
2023Censoring chemical data to mitigate dual use riskcited by 3
2023How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspectivecited by 3
2024TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Predictioncited by 3
2024Data-Efficient Molecular Generation with Hierarchical Textual Inversioncited by 3
2024Learning to Learn with Contrastive Meta-Objectivecited by 3
2021Embedding Adaptation is Still Needed for Few-Shot Learningcited by 2

50 most cited of 91 All 91 (JSON)