Toxicity Prediction by Multimodal Deep Learning

Abstract Representing molecules in the form of only one type of features and using those features to predict their activities is one of the most important approaches for machine-learning-based chemical-activity-prediction. For molecular activities like quantitative toxicity prediction, the performance depends on the type of features extracted and the machine learning approach used. For such cases, using one type of features and machine learning model restricts the prediction performance to specific representation and model used. In this paper, we study quantitative toxicity prediction and propose a machine learning model for the same. Our model uses an ensemble of heterogeneous predictors instead of typically using homogeneous predictors. The predictors that we use vary either on the type of features used or on the deep learning architecture employed. Each of these predictors presumably has its own strengths and weaknesses in terms of toxicity prediction. Our motivation is to make a combined model that utilizes different types of features and architectures to obtain better collective performance that could go beyond the performance of each individual predictor. We use six predictors in our model and test the model on four standard quantitative toxicity benchmark datasets. Experimental results show that our model outperforms the state-of-the-art toxicity prediction models in 8 out of 12 accuracy measures. Our experiments show that ensembling heterogeneous predictor improves the performance over single predictors and homogeneous ensembling of single predictors.The results show that each data representation or deep learning based predictor has its own strengths and weaknesses, thus employing a model ensembling multiple heterogeneous predictors could go beyond individual performance of each data representation or each predictor type.

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Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data

10.21079/11681/41302 ◽

2021 ◽

Author(s):

Gabriel Idakwo ◽

Sundar Thangapandian ◽

Joseph Luttrell ◽

Zhaoxian Zhou ◽

Chaoyang Zhang ◽

...

Keyword(s):

Deep Learning ◽

Androgen Receptor ◽

Learning Algorithms ◽

Structure Activity Relationship ◽

Activity Relationship ◽

Chemical Toxicity ◽

Toxicity Prediction ◽

Predictive Toxicology ◽

Structure Activity

Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.

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Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network

Molecules ◽

10.3390/molecules24183383 ◽

2019 ◽

Vol 24 (18) ◽

pp. 3383 ◽

Cited By ~ 5

Author(s):

Yuan ◽

Wei ◽

Guan ◽

Jiang ◽

Wang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Prediction Method ◽

Toxicity Prediction ◽

Learning Network ◽

Machine Learning Methods ◽

Molecular Information ◽

Molecular Toxicity ◽

Deep Learning Network

Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, the van der Waals force and hydrogen bond were calculated according to different descriptors of molecules, and multi-channel grids were generated, which could discover more detail and helpful molecular information for toxicity prediction. The generated grids were fed into a convolutional neural network to obtain the result. A Tox21 dataset was used for the evaluation. This dataset contains more than 12,000 molecules. It can be seen from the experiment that the proposed method performs better compared to other traditional deep learning and machine learning methods.

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DeepTox: Toxicity prediction using deep learning

Toxicology Letters ◽

10.1016/j.toxlet.2017.07.175 ◽

2017 ◽

Vol 280 ◽

pp. S69 ◽

Cited By ~ 7

Author(s):

Günter Klambauer ◽

Thomas Unterthiner ◽

Andreas Mayr ◽

Sepp Hochreiter

Keyword(s):

Deep Learning ◽

Toxicity Prediction

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DeepTox: Toxicity Prediction using Deep Learning

Frontiers in Environmental Science ◽

10.3389/fenvs.2015.00080 ◽

2016 ◽

Vol 3 ◽

Cited By ~ 247

Author(s):

Andreas Mayr ◽

Günter Klambauer ◽

Thomas Unterthiner ◽

Sepp Hochreiter

Keyword(s):

Deep Learning ◽

Toxicity Prediction

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Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.7b00244 ◽

2017 ◽

Vol 57 (11) ◽

pp. 2672-2685 ◽

Cited By ~ 74

Author(s):

Youjun Xu ◽

Jianfeng Pei ◽

Luhua Lai

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Acute Oral Toxicity ◽

Chemical Feature ◽

Toxicity Prediction ◽

Oral Toxicity

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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