Drug discovery with explainable artificial intelligence

Attention Is All You Need

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ImageNet classification with deep convolutional neural networks

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Long Short-Term Memory

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Semi-supervised classification with graph convolutional networks

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

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A Unified Approach to Interpreting Model Predictions

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Regularization and Variable Selection Via the Elastic Net

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Graph Attention NetworksPreprint

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TensorFlow: A system for large-scale machine learningPreprint

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Neural Machine Translation by Jointly Learning to Align and Translate

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Convolutional Neural Networks for Sentence Classification

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On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

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Graph Attention Networks

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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

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From local explanations to global understanding with explainable AI for trees

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Intriguing properties of neural networks

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Extended-Connectivity Fingerprints

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Dropout as a bayesian approximation: Representing model uncertainty in deep learning

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Neural Message Passing for Quantum ChemistryPreprint

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The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.

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Explanation in artificial intelligence: Insights from the social sciences

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VQA: Visual Question Answering

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Neural network ensembles

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Axiomatic attribution for deep networks

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Improved protein structure prediction using potentials from deep learning

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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

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Towards A Rigorous Science of Interpretable Machine Learning

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MoleculeNet: a benchmark for molecular machine learning

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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

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Nonlinear principal component analysis using autoassociative neural networks

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Human-level concept learning through probabilistic program induction

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Learning Important Features Through Propagating Activation Differences

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Convolutional Networks on Graphs for Learning Molecular Fingerprints

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Explaining Explanations: An Overview of Interpretability of Machine Learning

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Definitions, methods, and applications in interpretable machine learning

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Anchors: High-Precision Model-Agnostic Explanations

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European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”

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Probabilistic machine learning and artificial intelligence

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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

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DeepDTA: deep drug–target binding affinity prediction

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The rise of deep learning in drug discovery

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Molecular graph convolutions: moving beyond fingerprints

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Deep learning enables rapid identification of potent DDR1 kinase inhibitors

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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SmoothGrad: removing noise by adding noise

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Improved protein structure prediction using predicted interresidue orientations

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Sanity Checks for Saliency Maps

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

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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

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Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction

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Convolutional Neural Networks for Sentence ClassificationPreprint

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Interpretable Explanations of Black Boxes by Meaningful Perturbation

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Practical Variational Inference for Neural Networks

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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

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Junction Tree Variational Autoencoder for Molecular Graph GenerationPreprint

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Unmasking Clever Hans predictors and assessing what machines really learn

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Bootstrapping Regression Models

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VQA: Visual Question AnsweringPreprint

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K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

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Questioning the AI: Informing Design Practices for Explainable AI User Experiences

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QSAR without borders

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Rethinking drug design in the artificial intelligence era

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Top-Down Neural Attention by Excitation Backprop

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Low Data Drug Discovery with One-Shot Learning

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From machine learning to deep learning: progress in machine intelligence for rational drug discovery

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Deep Learning in Drug Discovery

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Machine Learning in Computer-Aided Synthesis Planning

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This Looks Like That: Deep Learning for Interpretable Image Recognition

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Central nervous system multiparameter optimization desirability: Application in drug discovery

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Towards better understanding of gradient-based attribution methods for Deep Neural NetworksPreprint

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A Survey of Methods for Explaining Black Box Models

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Towards Robust Interpretability with Self-Explaining Neural NetworksPreprint

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Unmasking Clever Hans predictors and assessing what machines really learn

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Explainability Methods for Graph Convolutional Neural Networks

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Information theory, distance matrix, and molecular branching

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Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions

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Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values

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Generating Visual Explanations

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Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

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“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models

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Accountability of AI Under the Law: The Role of Explanation

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Interpretable Counterfactual Explanations Guided by Prototypes

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Exploring Activity Cliffs in Medicinal Chemistry

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De Novo Design of Bioactive Small Molecules by Artificial Intelligence

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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

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Explanations based on the Missing: Towards Contrastive Explanations with Pertinent NegativesPreprint

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Target identification among known drugs by deep learning from heterogeneous networks

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The Blood–Brain Barrier (BBB) Score

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To Trust Or Not To Trust A ClassifierPreprint

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