Massively Multitask Networks for Drug Discovery
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Summary
The results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process and investigate several aspects of the multitask framework.
- Type
- preprint
- Published
- 2015-02-06
- Cited by
- 502
- References
- 26
- Access
- Open access
- OpenAlex
- https://openalex.org/W1738019091
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2127453
Keywords
Computer science, Massively parallel, Multi-task learning, Machine learning, Task (project management)
References
- Learning representations by back-propagating errors
- Multi-task Neural Networks for QSAR Predictions
- Rectified Linear Units Improve Restricted Boltzmann Machines
- Variations of Box Plots
- Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data
- Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
- Extended-Connectivity Fingerprints
- Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?
- Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships
- Collaborative Filtering on a Family of Biological Targets
- Recommendations for evaluation of computational methods
- The Influence Relevance Voter: An Accurate And Interpretable Virtual High Throughput Screening Method
- Ligand-Based Virtual Screening Using Bayesian Networks
- Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules
- Virtual screening of chemical libraries
- New types of deep neural network learning for speech recognition and related applications: an overview
- PubChem's BioAssay Database
- Chemical Similarity Searching
- Going deeper with convolutions
- A unified architecture for natural language processing: deep neural networks with multitask learning
Cited by
- Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models
- Identifying compound efficacy targets in phenotypic drug discovery.
- Label-free screening of single biomolecules through resistive pulse sensing technology for precision medicine applications
- Toxicity Prediction using Deep Learning
- Providing data science support for systems pharmacology and its implications to drug discovery
- ENISI multiscale modeling of mucosal immune responses driven by high performance computing
- AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
- Digital health revolution: perfect storm or perfect opportunity for pharmaceutical R&D?
- Computer vision for high content screening
- Modeling-Enabled Systems Nutritional Immunology
- Prediction of Compounds Activity in Nuclear Receptor Signaling and Stress Pathway Assays Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Molecular graph convolutions: moving beyond fingerprints
- Research Update: Computational materials discovery in soft matter
- Materials science with large-scale data and informatics: Unlocking new opportunities
- Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?
- Docking and Virtual Screening Strategies for GPCR Drug Discovery.
- A renaissance of neural networks in drug discovery
- Concrete Problems in AI Safety
- Modeling Industrial ADMET Data with Multitask Networks
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