How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
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- Type
- preprint
- Published
- 2017-10-05
- Cited by
- 48
- References
- 38
- Access
- Open access
- OpenAlex
- https://openalex.org/W2761072963
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3987691
Keywords
Deep learning, Computer science, Artificial intelligence, Domain (mathematical analysis), Representation (politics)
References
- Toxicophores: groups and metabolic routes associated with increased safety risk.
- Multi-task Neural Networks for QSAR Predictions
- cuDNN: Efficient Primitives for Deep Learning
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Massively Multitask Networks for Drug Discovery
- Pharmacophore modeling and applications in drug discovery: challenges and recent advances.
- 3-D pharmacophores in drug discovery.
- Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships
- ITERATIVE PARTIAL EQUALIZATION OF ORBITAL ELECTRONEGATIVITY – A RAPID ACCESS TO ATOMIC CHARGES
- Computational toxicology in drug development.
- Machine learning of molecular electronic properties in chemical compound space
- Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules
- Site of Reactivity Models Predict Molecular Reactivity of Diverse Chemicals with Glutathione
- Going deeper with convolutions
- Object recognition from local scale-invariant features
- DeepTox: Toxicity Prediction using Deep Learning
- Deep Learning in Drug Discovery
- Deep Learning for Drug-Induced Liver Injury
- Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
- Molecular graph convolutions: moving beyond fingerprints
Cited by
- SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
- ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction
- A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
- The rise of deep learning in drug discovery.
- Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction
- Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era
- Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction
- IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
- An overview of neural networks for drug discovery and the inputs used
- Learning Molecule Drug Function from Structure Representations with Deep Neural Networks or Random Forests
- SMILES2vec: Predicting Chemical Properties from Text Representations
- Drug cell line interaction prediction
- Formatting biological big data for modern machine learning in drug discovery
- How Convolutional Neural Networks Diagnose Plant Disease
- Deep Learning in Chemistry
- KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
- Toxicity Prediction by Multimodal Deep Learning
- Deep learning in drug discovery: opportunities, challenges and future prospects.
- Sparse hierarchical representation learning on molecular graphs
- Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests
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