Deep Learning as an Opportunity in Virtual Screening

By Thomas Unterthiner, Andreas Mayr, Jörg Kurt Wegner. 2015

183 citations recorded by the source corpus.

Subfields: Computational Chemistry & Drug Discovery

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In Computational Chemistry & Drug Discovery

Citations in the corpus

Convolutional Networks on Graphs for Learning Molecular Fingerprints (2015, methodology): “For example, [28] used a fingerprint vector of size 43,000, after having removed rarely-occurring features.”
Opportunities and obstacles for deep learning in biology and medicine (2017, background): “[19]) explored the effects of jointly modeling far more targets than the Merck challenge [301,302], with Ramsundar et al.”
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, methodology): “That work was followed by Untherhiner et al. [11, 12] and Ramsundar et al. [13] that demonstrated the MT-DNN technique scales to large biochemical databases such as PubChem Bioassays [14] and ChEMBL [15].”
DeepTox: Toxicity Prediction using Deep Learning (2016, methodology): “Deep Learning has already been applied to predict the outcome of biological assays (Dahl et al., 2014; Unterthiner et al., 2014, 2015; Ma et al., 2015), which made it our prime candidate for toxicity prediction.”
Deep learning with feature embedding for compound-protein interaction prediction (2016, methodology): “The approaches proposed in [27] [28] [29] only used the hand-designed features of compounds, but did not take the feature information of targets into account.”
Modeling, Inference and Optimization With Composable Differentiable Procedures (2016, methodology): “For example, [108] used a fingerprint vector of size 43,000, after having removed rarely-occurring features.”
Predicting protein–ligand affinity with a random matrix framework (2016, methodology): “Figure 2a shows that our algorithm has a mean AUC of 0.9, surpassing methods commonly used in the literature, which have a mean AUC of 0.7 − 0.8 [30].”
Accelerating Drugs Discovery with Deep Reinforcement Learning: An Early Approach (2018, methodology): “They have been also successfully applied in drug discovery [6, 12, 33], including application such as QSAR [32], genomics [1, 61], and VS [8, 58].”