Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

Chen, Jiarui; Si, Yain-Whar; Un, Chon-Wai; Siu, Shirley W. I.

doi:10.1186/s13321-021-00570-8

Cited by 38 publications

(26 citation statements)

References 48 publications

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“…218 To address the lack of annotated toxicity data, they proposed using semisupervised learning algorithms, such as the Mean Teacher semisupervised learning algorithm, in combination with GCN. 218 Using Tox21 data, the authors found that the semisupervised learning-GCN models outperformed the GCN models trained by supervised learning and conventional machine-learning methods by 6% on 12 toxicological end points, achieving an average AUROC score of 0.757 in the test set. 218 They also found that unlabeled data was advantageous for model training, and the optimal unannotated to annotated data ratio ranged from 1:1 to 4:1.…”

Section: Gnnmentioning

confidence: 99%

“…218 Using Tox21 data, the authors found that the semisupervised learning-GCN models outperformed the GCN models trained by supervised learning and conventional machine-learning methods by 6% on 12 toxicological end points, achieving an average AUROC score of 0.757 in the test set. 218 They also found that unlabeled data was advantageous for model training, and the optimal unannotated to annotated data ratio ranged from 1:1 to 4:1. 218 The study demonstrates the success of semisupervised learning in chemical toxicity prediction and suggests potential applications in other chemical property prediction tasks.…”

Section: Gnnmentioning

confidence: 99%

“…218 They also found that unlabeled data was advantageous for model training, and the optimal unannotated to annotated data ratio ranged from 1:1 to 4:1. 218 The study demonstrates the success of semisupervised learning in chemical toxicity prediction and suggests potential applications in other chemical property prediction tasks. However, the authors note limitations in terms of data diversity, interpretability of the graph convolution model, and the unresolved issue of activity cliffs.…”

Section: Gnnmentioning

confidence: 99%

See 2 more Smart Citations

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug–drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

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Section: Gnnmentioning

confidence: 99%

Section: Gnnmentioning

confidence: 99%

Section: Gnnmentioning

confidence: 99%

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A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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“…GCN is already well applied to predicting the compound property, and molecular fingerprint (Kojima et al, 2020;J. Chen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…The Graph Convolutional Network (GCN) is a kind of deep learning that can use nodes to contain feature information and edges to contain spatial information between nodes, which is a popular method in prediction relationships(S. Zhang et al ., 2019). GCN is already well applied to predicting the compound property, and molecular fingerprint(Kojima et al ., 2020; J. Chen et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Accurate Protein-Ligand Interaction Prediction

Zhang¹,

Saravanan

Zhang

2023

Preprint

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The core of large-scale drug virtual screening is to accurately and efficiently select the binders with high affinity from large libraries of small molecules in which non-binders are usually dominant. The protein pocket, ligand spatial information, and residue types/atom types play a pivotal role in binding affinity. Here we used the pocket residues or ligand atoms as nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, we find that the model with pre-trained molecular vectors performs better than the onehot representation. The main advantage of DeepBindGCN is that it is non-dependent on docking conformation and concisely keeps the spatial information and physical-chemical feature. Notably, the DeepBindGCN_BC has high precision in many DUD.E datasets, and DeepBindGCN_RG achieve a very low RMSE value in most DUD.E datasets. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline by integrating DeepBindGCN_BC, DeepBindGCN_RG, and other methods to identify strong binding affinity compounds. In addition, a DeepBindGCN_RG_x model has been used for comparing performance with other methods in PDBbind v.2016 and v.2013 core set. It is the first time that a non-complex dependent model achieves an RMSE value of 1.3843 and Pearson-R value of 0.7719 in the PDBbind v.2016 core set, showing comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. Our DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.

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ToxMPNN: A deep learning model for small molecule toxicity prediction

Zhou,

Ning,

Tan

et al. 2024

J of Applied Toxicology

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Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized into seven toxicity classes, namely, carcinogenicity and mutagenicity, acute oral toxicity, respiratory toxicity, irritation and corrosion, cardiotoxicity, CYP450, and endocrine disruption. In addition, a binary classification Common‐Toxicity task was added based on the aforementioned dataset. To improve the performance of the models, we added marketed drugs as negative samples. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common‐Toxicity task but also enhances the stability of the model prediction. It shows that the graph‐based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs.

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Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

Cited by 38 publications

References 48 publications

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Accurate Protein-Ligand Interaction Prediction

ToxMPNN: A deep learning model for small molecule toxicity prediction

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