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Motivation: Many high-performance DTA models have been proposed, but they are mostly black-box and thus lack human interpretability.
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DeepDTA: deep drug–target binding affinity prediction
Öztürk, H. et al · 2018
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Ryu, S. et al · 2018
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Edge Attention-based Multi-Relational Graph Convolutional Networks
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Sutton, R. S. and Barto, A. G. (2018) · 2018
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Tsang, M. et al · 2018
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Graph Attention Networks
Veličković, P. et al · 2018
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DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models
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Drug–target affinity prediction using graph neural network and contact maps
Jiang, M. et al · 2020
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Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Mothilal, R. K. et al · 2020
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GraphDTA: Predicting drug-target binding affinity with graph neural networks
Nguyen, T. et al · 2020
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Explaining Deep Graph Networks with Molecular Counterfactuals
Numeroso, D. and Bacciu, D. (2020) · 2020
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Interpretation of compound activity predictions from complex machine learning models using local approximations and shapley values
Rodríguez-Pérez, R. and Bajorath, J. (2020) · 2020
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Predicting drug–protein interaction using quasi-visual question answering system
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MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks
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