Deep Learning in Drug Discovery

By Erik Gawehn, Jan A. Hiss, Gisbert Schneider. 2016

644 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (2017, methodology): “ered features developed using domain knowledge. Recent work have also started using deep neural networks (DNN) [9, 19, 24, 27], that are on average, typically more accurate than traditional ML models [12, 14]. 1.1 Limitations of Feature Engineering and Data Challenges Compared to modern deep learnin”
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017, background): “Nevertheless, given that we have shown in this work that our model can rediscover those needles, and other recent developments, 31,79 we believe that deep neural networks can be complementary to established approaches in drug discovery.”
Deep learning for computational biology (2016, neutral): “This approach allows using higher learning rates and hence also accelerates training.”
eDoctor: machine learning and the future of medicine (2018, methodology): “ML has been applied to medical image segmentation, whereby the computer learns to separate structures and organs [47 – 49], as well as registration, computer-aided detection (CAD) [50 – 52] and brain functional analyses [41,42].”
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, background): “In recent years, many deep learning approaches have recently been developed and recruited for omics data processing [18] as well as drug discovery [19], and these approaches seem to be able to overcome limitations.”
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive (2019, background): “, speech recognition [1], image recognition [2], drug discovery [3] and gene analysis for cancer research [4], [5].”
A renaissance of neural networks in drug discovery (2016, background): “Although first publications on the use of deep learning in the field of drug discovery appeared very recently [47, 100 – 102], some of the key ideas underlying the concept of deep learning have already been used for building QSAR models.”
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].”
Deep Physical Informed Neural Networks for Metamaterial Design (2020, background): “The dramatic growth of available data and the evolution of deep learning [5] revolutionize our understanding of the physical world in modern application areas such as image recognition [6], drug discovery [7], and bioinformatics [8].”
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale (2018, background): “deep learning in drug discovery [19], materials design [16,18,128], microscopy and spectroscopy [129-134], and other physics-related domains [135-137].”