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Explainable Artificial Intelligence in Drug Discovery: Bridging Predictive Power and Mechanistic Insight

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Mitigating Molecular Aggregation in Drug Discovery With Predictive Insights From Explainable AI

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Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery

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Explainable Fuzzy Systems

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Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification

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Explainable Artificial Intelligence in Drug Discovery

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Generative chemistry: drug discovery with deep learning generative models

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Explaining prediction models and individual predictions with feature contributions

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Counterfactual explanations and how to find them: literature review and benchmarking

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A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts

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Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening

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Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks

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Measuring the Quality of Explanations: The System Causability Scale (SCS)

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Logic Explained Networks

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CMINNs: Compartment model informed neural networks — Unlocking drug dynamics

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Unleashing the power of generative AI in drug discovery

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MolAnchor method for explaining compound predictions based on substructures

inferredcites →https://doi.org/10.1016/j.inffus.2019.12.012openalex-snapshot

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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A comprehensive study on fidelity metrics for XAI

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A survey on the suitability of risk identification techniques in the current networked environment

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Evaluating the necessity of the multiple metrics for assessing explainable AI: A critical examination

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A comprehensive survey on contrastive learning

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Explaining nonlinear classification decisions with deep Taylor decomposition

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Quantitative evaluation of explainable graph neural networks for molecular property prediction

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Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research

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Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks

inferredcites →https://doi.org/10.1021/acs.jcim.0c00866openalex-snapshot

Explainable Deep Relational Networks for Predicting Compound–Protein Affinities and Contacts

inferredcites →https://doi.org/10.1021/acs.jcim.0c01344openalex-snapshot

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

inferredcites →https://doi.org/10.1021/acs.jcim.1c00519openalex-snapshot

Explainable Molecular Sets: Using Information Theory to Generate Meaningful Descriptions of Groups of Molecules

inferredcites →https://doi.org/10.1021/acs.jcim.1c01263openalex-snapshot

Interpretation of Structure–Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence

inferredcites →https://doi.org/10.1021/acs.jcim.1c01467openalex-snapshot

Unified Deep Learning Model for Multitask Reaction Predictions with Explanation

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An In Silico Explainable Multiparameter Optimization Approach for De Novo Drug Design against Proteins from the Central Nervous System

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Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models

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TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity

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Interpretable Molecular Property Predictions Using Marginalized Graph Kernels

inferredcites →https://doi.org/10.1021/acs.jcim.3c00689openalex-snapshot

pBRICS: A Novel Fragmentation Method for Explainable Property Prediction of Drug-Like Small Molecules

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AttenSyn: An Attention-Based Deep Graph Neural Network for Anticancer Synergistic Drug Combination Prediction

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Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features

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SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction

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A Perspective on Explanations of Molecular Prediction Models

inferredcites →https://doi.org/10.1038/s41467-019-08987-4openalex-snapshot

Unmasking Clever Hans predictors and assessing what machines really learn

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Discovery of a structural class of antibiotics with explainable deep learning

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Large language models in medicine

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An explainable deep learning platform for molecular discovery

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Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations

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A novel framework based on explainable AI and genetic algorithms for designing neurological medicines

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inferredcites →https://doi.org/10.1038/s41746-023-00837-4openalex-snapshot

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inferredcites →https://doi.org/10.1038/s42004-024-01155-wopenalex-snapshot

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inferredcites →https://doi.org/10.1038/s42256-020-00236-4openalex-snapshot

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AI for radiographic COVID-19 detection selects shortcuts over signal

inferredcites →https://doi.org/10.1038/s42256-021-00418-8openalex-snapshot

Geometric deep learning on molecular representations

inferredcites →https://doi.org/10.1039/d1sc05180fopenalex-snapshot

MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

inferredcites →https://doi.org/10.1039/d1sc05259dopenalex-snapshot

Model agnostic generation of counterfactual explanations for molecules

inferredcites →https://doi.org/10.1089/big.2016.0007openalex-snapshot

A Framework for Considering Comprehensibility in Modeling

inferredcites →https://doi.org/10.1093/bioinformatics/btac405openalex-snapshot

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inferredcites →https://doi.org/10.1093/bioinformatics/btad207openalex-snapshot

ArkDTA: attention regularization guided by non-covalent interactions for explainable drug–target binding affinity prediction

inferredcites →https://doi.org/10.1101/2024.10.17.618804openalex-snapshot

Knowledge Graphs and Explainable AI for Drug Repurposing on Rare Diseases

inferredcites →https://doi.org/10.1109/access.2024.3373195openalex-snapshot

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

inferredcites →https://doi.org/10.1109/access.2024.3467062openalex-snapshot

Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions

inferredcites →https://doi.org/10.1109/cvpr.2019.01103openalex-snapshot

Explainability Methods for Graph Convolutional Neural Networks

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Proceedings of IEEE International Conference on Computer Vision

inferredcites →https://doi.org/10.1109/iccv.2017.371openalex-snapshot

Interpretable Explanations of Black Boxes by Meaningful Perturbation

inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

inferredcites →https://doi.org/10.1109/icdm.2002.1183878openalex-snapshot

Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002

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Contrastive Explanations In Neural Networks

inferredcites →https://doi.org/10.1109/ijcnn.2005.1556327openalex-snapshot

Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005

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MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks

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Prototype-Based Interpretable Graph Neural Networks

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GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

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Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks

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A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

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Higher-Order Explanations of Graph Neural Networks via Relevant Walks

inferredcites →https://doi.org/10.1111/cbdd.14262openalex-snapshot

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inferredcites →https://doi.org/10.1145/3394486.3403085openalex-snapshot

XGNN: Towards Model-Level Explanations of Graph Neural Networks

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inferredcites →https://doi.org/10.1145/3708359.3712095openalex-snapshot

Counterfactual Explanations May Not Be the Best Algorithmic Recourse Approach

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Benchmarking the most popular XAI used for explaining clinical predictive models: Untrustworthy but could be useful

inferredcites →https://doi.org/10.1186/s12859-023-05460-4openalex-snapshot

SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features

inferredcites →https://doi.org/10.1186/s12915-024-01981-3openalex-snapshot

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs

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Understanding predictions of drug profiles using explainable machine learning models

inferredcites →https://doi.org/10.1186/s13321-020-00460-5openalex-snapshot

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inferredcites →https://doi.org/10.1186/s13321-022-00673-wopenalex-snapshot

XSMILES: interactive visualization for molecules, SMILES and XAI attribution scores

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ProtGNN: Towards Self-Explaining Graph Neural Networks

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