mlsanityData to 7 Oct 2026

Drug discovery with explainable artificial intelligence

arXiv 2007.00523 ↗First posted 1 Jul 2020cs.AI cs.LG stat.MLcited by 118

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Abstract (arXiv)

Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical entities with bespoke properties. Despite the growing number of successful prospective applications, the underlying mathematical models often remain elusive to interpretation by the human mind. There is a demand for 'explainable' deep learning methods to address the need for a new narrative of the machine language of the molecular sciences. This review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and dares a forecast of the future opportunities, potential applications, and remaining challenges.

Key facts. Cited by 118 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2020.

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Citing papers per year: 2021: 11; 2022: 29; 2023: 30; 2024: 23; 2025: 10; 2026: 15.

118 papers.

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  1. 2021Geometric Deep Learning on Molecular Representationscited by 113
  2. 2021Trustworthy AI: A Computational Perspectivecited by 93
  3. 2021Graph Representation Learning in Biomedicinecited by 79
  4. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  5. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  6. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  7. 2023Adversarial attacks and defenses in explainable artificial intelligence: A surveycited by 47
  8. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  9. 2023Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorialcited by 27
  10. 2022Graph Neural Networks for Moleculescited by 21
  11. 2023Distill n' Explain: explaining graph neural networks using simple surrogatescited by 18
  12. 2023The role of causality in explainable artificial intelligencecited by 17
38 more
  1. 2021Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Predictioncited by 15
  2. 2022DORA: Exploring Outlier Representations in Deep Neural Networkscited by 14
  3. 2022Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discoverycited by 13
  4. 2022Molecule Generation for Drug Design: a Graph Learning Perspectivecited by 13
  5. 2023t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule Generationcited by 13
  6. 2023Theoretical Behavior of XAI Methods in the Presence of Suppressor Variablescited by 12
  7. 2023DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Designcited by 12
  8. 2023Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Frameworkcited by 11
  9. 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
  10. 2024Interpretability Needs a New Paradigmcited by 10
  11. 2024What is it for a Machine Learning Model to Have a Capability?cited by 10
  12. 2022ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Predictioncited by 8
  13. 2022Molecular Joint Representation Learning via Multi-modal Informationcited by 8
  14. 2021Principled Hyperedge Prediction with Structural Spectral Features and Neural Networkscited by 7
  15. 2022MEGAN: Multi-Explanation Graph Attention Networkcited by 7
  16. 2023Categorical Foundations of Explainable AI: A Unifying Theorycited by 7
  17. 2023Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Modelscited by 7
  18. 2024Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Spacecited by 7
  19. 2024Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Featurescited by 7
  20. 2024What fifty-one years of Linguistics and Artificial Intelligence research tell us about their correlation: A scientometric analysiscited by 7
  21. 2022Deep Feature Screening: Feature Selection for Ultra High-Dimensional Data via Deep Neural Networkscited by 6
  22. 2023Knowledge-augmented Graph Machine Learning for Drug Discovery: A Surveycited by 6
  23. 2024Why do explanations fail? A typology and discussion on failures in XAIcited by 6
  24. 2021Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysiscited by 5
  25. 2022GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactionscited by 5
  26. 2023Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical spacecited by 5
  27. 2024How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Surveycited by 5
  28. 2024GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanationscited by 5
  29. 2022Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systemscited by 4
  30. 2022Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Datacited by 4
  31. 2023Identifying Important Sensory Feedback for Learning Locomotion Skillscited by 4
  32. 2023Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Surveycited by 4
  33. 2023Generative Explanations for Graph Neural Network: Methods and Evaluationscited by 4
  34. 2024Remedying uncertainty representations in visual inference through Explaining-Away Variational Autoencoderscited by 4
  35. 2024Explainable AI needs formalizationcited by 4
  36. 2025B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Datacited by 4
  37. 2022Interpretation Quality Score for Measuring the Quality of interpretability methodscited by 3
  38. 2022Structure-based drug discovery with deep learningcited by 3

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