Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships

By Junshui Ma, Robert P. Sheridan, Andy Liaw, George E. Dahl, V. Svetnik. 2015

1,045 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Molecular graph convolutions: moving beyond fingerprints (2016, methodology): “We follow the common approach (Ramsundar et al., 2015; Ma et al., 2015; Unterthiner et al., 2015) of a small number of fully connected layers on top of the molecule-level features followed by standard softmax classiﬁcation.”
Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “The sponsors noted drastic improvements over a random forest baseline, remarking "we have seldom seen any method in the past 10 years that could consistently outperform [random forest] by such a margin" [300].”
Massively Multitask Networks for Drug Discovery (2015, background): “Additional work at Merck analyzed the choice of hyper-parameters when training single-and multitask networks and showed improvement over random forest models (Ma et al., 2015).”
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, methodology): “Recently, deep neural networks have been shown to out-perform random forests and SVMs for QSAR and ligand-based virtual screening [9, 10, 11].”
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey (2018, background): “in reconstruction of brain circuits [2]; analysis of mutations in DNA [3]; prediction of structure-activity of potential drug molecules [4], and analyzing the particle accelerator data [5] [6].”
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 Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records (2016, neutral): “Deep learning was recently applied to medicine and genomics to reconstruct brain circuits 35 and to predict the activity of potential drug molecules 36 , the effects of mutations in non-coding DNA on gene expressions 37,38 , and the sequence specificities of DNA and RNA-binding proteins 39 .”
A renaissance of neural networks in drug discovery (2016, background): “…epitopes to major histocompatibility complex class II [45], the activities of inhalation anesthetics [46], etc. Recent studies using the Kaggle benchmark data sets have shown that Bayesian neural networks generate models with similar prediction accuracy to those of deep learning methods [47].”