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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.
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Inside the mind of a medicinal chemist: The role of human bias in compound prioritization during drug discovery
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Scalable gaussian process regression using deep neural networks
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Gawehn, E., Hiss, J. A. & Schneider, G · 2016
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Local explanation methods for deep neural networks lack sensitivity to parameter values
Adebayo, J., Gilmer, J., Goodfellow, I. & Kim, B · 2018
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Structural and physico-chemical interpretation (SPCI) of QSAR models and its comparison with matched molecular pair analysis
Polishchuk, P · 2016
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Investigating the mechanisms of bioconcentration through QSAR classification trees
Grisoni, F., Consonni, V., Vighi, M., Villa, S. & Todeschini, R · 2016
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Central nervous system multiparameter optimization desirability: Application in drug discovery
Wager, T. T., Hou, X., Verhoest, P. R. & Villalobos, A · 2016
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Understanding the roles of the “two QSARs”
Fujita, T. & Winkler, D. A · 2016
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"why should i trust you?" explaining the predictions of any classifier
Ribeiro, M. T., Singh, S. & Guestrin, C · 2016
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Are you visually intelligent? What you don’t see is as important as what you do see
Herman, A · 2016
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R. & Jaakkola, T · 2018
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The use of structural alerts to avoid the toxicity of pharmaceuticals
Limban, C · 2018
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Towards robust interpretability with self-explaining neural networks
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Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
Li, O., Liu, H., Chen, C. & Rudin, C · 2018
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Gilpin, L. H · 2018
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Bayesian uncertainty estimation for batch normalized deep networks
Teye, M., Azizpour, H. & Smith, K · 2018
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Jiang, H., Kim, B., Guan, M. & Gupta, M · 2018
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Molecular similarity-based domain applicability metric efficiently identifies out-of-domain compounds
Liu, R. & Wallqvist, A · 2018
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Deep confidence: A computationally efficient framework for calculating reliable prediction errors for deep neural networks
Cortés-Ciriano, I. & Bender, A · 2018
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Segler, M. H., Kogej, T., Tyrchan, C. & Waller, M. P · 2018
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Nagarajan, D · 2018
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Müller, A. T., Hiss, J. A. & Schneider, G · 2018
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Wishart, D. S · 2018
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Concepts of artificial intelligence for computer-assisted drug discovery
Yang, X., Wang, Y., Byrne, R., Schneider, G. & Yang, S · 2019
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Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A · 2019
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A graph-convolutional neural network model for the prediction of chemical reactivity
Coley, C. W · 2019
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Machine learning consensus to predict the binding to the Androgen receptor within the CoMPARA project
Grisoni, F., Consonni, V. & Ballabio, D · 2019
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NP-scout: Machine learning approach for the quantification and visualization of the natural product-likeness of small molecules
Chen, Y., Stork, C., Hirte, S. & Kirchmair, J · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
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Mind and machine in drug design
Schneider, G · 2019
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Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R. & Yu, B · 2019
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Unmasking clever Hans predictors and assessing what machines really learn
Lapuschkin, S · 2019
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Explanation in artificial intelligence: Insights from the social sciences
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Advancing computational toxicology in the big data era by artificial intelligence: Data-driven and mechanism-driven modeling for chemical toxicity
Ciallella, H. L. & Zhu, H · 2019
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Interpretation of QSAR models by coloring atoms according to changes in predicted activity: How robust is it?
Sheridan, R. P · 2019
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Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders
Manica, M · 2019
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Interpretable deep learning in drug discovery , 331–345 (Springer, 2019)
Preuer, K., Klambauer, G., Rippmann, F., Hochreiter, S. & Unterthiner, T · 2019
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Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values
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Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction
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