Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P.; Ekins, Sean

doi:10.1021/acs.molpharmaceut.7b00578

Cited by 288 publications

(266 citation statements)

References 124 publications

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“…Although there are several chemistry problems where DNNs outperform other shallow machine learning methods 49,59,60 , here the MFP+RF performed best with the small dataset of 676 molecules in the 5-and 12-class predictions. However, in the 3class task with the small dataset, and all the tasks with the large dataset, the two models produced accuracies that were nearly indistinguishable.…”

Section: Discussionmentioning

confidence: 85%

Learning Drug Function from Chemical Structure with Convolutional Neural Networks and Random Forests

Meyer

Liu

Miller

et al. 2018

Preprint

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Empirical testing of chemicals for drug efficacy costs many billions of dollars every year. The ability to predict the action of molecules in silico would greatly increase the speed and decrease the cost of prioritizing drug leads. Here, we asked whether drug function, defined as MeSH "Therapeutic Use" classes, can be predicted from only chemical structure. We evaluated two chemical structure-derived drug classification methods, chemical images with convolutional neural networks and molecular fingerprints with random forests, both of which outperformed previous predictions that used druginduced transcriptomic changes as chemical representations. This suggests that a chemical's structure contains at least as much information about its therapeutic use as the transcriptional cellular response to that chemical. Further, because training data based on chemical structure is not limited to a small set of molecules for which transcriptomic measurements are available, our strategy can leverage more training data to significantly improve predictive accuracy to 83-88%. Finally, we explore use of these models for prediction of side effects and drug repurposing opportunities, and demonstrate the effectiveness of this modeling strategy for multi-label classification.

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Section: Discussionmentioning

confidence: 85%

Learning Drug Function from Chemical Structure with Convolutional Neural Networks and Random Forests

Meyer

Liu

Miller

et al. 2018

Preprint

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“…[71] who showed that positive transfer learning is conditioned by sharing a significant amount of input data whose labels are correlated. In general, other studies [72,73,74,75] reported that FNN performs the best for a variety of chemoinformatics problem, but only for some metrics and datasets.…”

Section: Deep Learning For Chemoinformaticsmentioning

confidence: 92%

Evaluation of network architecture and data augmentation methods for deep learning in chemogenomics

Playe

Stoven

2019

Preprint

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Among virtual screening methods that have been developed to facilitate the drug discovery process, chemogenomics presents the particularity to tackle the question of predicting ligands for proteins, at at scales both in the protein and chemical spaces. Therefore, in addition to to predict drug candidates for a given therapeutic protein target, like more classical ligand-based or receptor-based methods do, chemogenomics can also predict off-targets at the proteome level, and therefore, identify potential side-effects or drug repositioning opportunities. In this study, we study and compare machinelearning and deep learning approaches for chemogenomics, that are applicable to screen large sets of compounds against large sets of druggable proteins. State-of-the-art drug chemogenomics methods rely on expert-based chemical and protein descriptors or similarity measures. The recent development of deep learning approaches enabled to design algorithms that learn numerical abstract representations of molecular graphs and protein sequences in an end-to-end fashion, i.e., so that the learnt features optimise the objective function of the drug-target interaction prediction task. In this paper, we address drug-target interaction prediction at the druggable proteome-level, with what we define as the chemogenomic neuron network. This network consists of a feed-forward neuron network taking as input the combination of molecular and protein representations learnt by molecular graph and protein sequence encoders. We first propose a standard formulation of this chemogenomic neuron network. Then, we compare the performances of the standard chemogenomic network to reference deep learning or shallow (machine-learning without deep learning) methods. In particular, we show that such a representation learning approach is competitive with state-of-the-art chemogenomics with shallow methods, but not ultimately superior. We evaluate the most promising neuron network architectures and data augmentation techniques, such as multi-view and transfer learning, to improve the prediction performance of the chemogenomic network. Our results shed new insights on the design of chemogenomics approaches based on representation learning algorithms. Most importantly, we conclude from our observations that a promising research direction is to integrate heterogeneous sources of data such as various bioactivity datasets, or independently, multiple molecule and protein attribute views, instead of focusing on sophisticated, yet intuitively relevant, encoder's neuron network architecture.

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“…Deep neural networks model is similar to neural networks in the brain. It stimulates the process of signal conduction between neurons (Junshui, Sheridan, Andy, Dahl, & Vladimir, 2015;Korotcov, Tkachenko, Russo, & Ekins, 2017;Lecun, Bengio, & Hinton, 2015;Ramsundar et al, 2017). Three types of layers constitute the whole network, as shown in Figure S1.…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…Recall reflects the ability of a model to find all the right cases within a data set and precision expresses the proportion of the data points are correctly predicted as actives. F1 score is the harmonic mean of precision and recall (Cai et al, 2018;Korotcov et al, 2017;Podlewska, Czarnecki, Kafel, & Bojarski, 2017). In general, accuracy is used to evaluate the performance of the classification model.…”

Section: Performance Evaluationmentioning

confidence: 99%

Prediction of hERG K+ channel blockage using deep neural networks

et al. 2019

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Human ether‐a‐go‐go‐related gene (hERG) K+ channel blockage may cause severe cardiac side‐effects and has become a serious issue in safety evaluation of drug candidates. Therefore, improving the ability to avoid undesirable hERG activity in the early stage of drug discovery is of significant importance. The purpose of this study was to build predictive models of hERG activity by deep neural networks. For each combination of sampling methods and descriptors, deep neural networks with different architectures were implemented to build classification models. The optimal model M15 with three hidden layers, undersampling method, and 2D descriptors yielded the prediction accuracy of 0.78 and F1 score of 0.75 on the test set as well as accuracy of 0.77 and F1 score of 0.34 on the external validation set, outperforming the other 35 models including 9 random forest models. Particularly, the optimal model M15 achieved the highest F1 score and the second highest accuracy when compared with other five methods from four groups using different machine learning algorithms with the same external validation set. It can be believed that this model has powerful capability on prediction of hERG toxicity, which is of great benefit for developing novel drug candidates.

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Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Cited by 288 publications

References 124 publications

Learning Drug Function from Chemical Structure with Convolutional Neural Networks and Random Forests

Learning Drug Function from Chemical Structure with Convolutional Neural Networks and Random Forests

Evaluation of network architecture and data augmentation methods for deep learning in chemogenomics

Prediction of hERG K+ channel blockage using deep neural networks

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