eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
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- Type
- article
- Published
- 2019-01-08
- Cited by
- 156
- References
- 94
- Access
- Open access
- OpenAlex
- https://openalex.org/W30621790
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:57617483
Keywords
Computer science
References
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- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- In Silico Prediction of Chemical Acute Oral Toxicity Using Multi-Classification Methods
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- Imaging of the fish embryo model and applications to toxicology. (Imagerie du modèle embryon de poisson : application à la toxicologie du développement)
- Applying knowledge-driven mechanistic inference to toxicogenomics
- An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation.
- Toxicity prediction of small drug molecules of androgen receptor using multilevel ensemble model
- The Application of Negative Design to Design More Desirable Virtual Screening Library.
- Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques
- Drugs Classifier System Based on Machine Learning Algorithms
- Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes
- Spectrum of deep learning algorithms in drug discovery
- Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.
- Remodelling structure-based drug design using machine learning.
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery
- Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products
- Predicting biochemical and physiological effects of natural products from molecular structures using machine learning.
- Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery
- Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology
- Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network
- Trends in Deep Learning for Property-driven Drug Design.
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