Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
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- Type
- article
- Published
- 2018-02-28
- Cited by
- 1,978
- References
- 70
- Access
- Open access
- OpenAlex
- https://openalex.org/W3098269892
Keywords
Representation (politics), Chemical space, Computer science, Encoder, Set (abstract data type)
References
- Estimation of the size of drug-like chemical space based on GDB-17 data
- Efficient Global Optimization of Expensive Black-Box Functions
- Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
- Theano: new features and speed improvements
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review
- SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
- Chemical Space Travel
- Strategy To Discover Diverse Optimal Molecules in the Small Molecule Universe
- Extended-Connectivity Fingerprints
- The chemical space project.
- Virtual screening: an endless staircase?
- Computational Design and Selection of Optimal Organic Photovoltaic Materials
- Recognizing Pitfalls in Virtual Screening: A Critical Review
- A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
- DrugBank 4.0: shedding new light on drug metabolism
- ZINC: A Free Tool to Discover Chemistry for Biology
- Quantifying the chemical beauty of drugs
- Prediction of Physicochemical Parameters by Atomic Contributions
- Virtual screening of chemical libraries
Cited by
- Bidirectional Molecule Generation with Recurrent Neural Networks
- Population-Based Black-Box Optimization for Biological Sequence Design
- Prediction of pKa Values for Druglike Molecules Using Semiempirical Quantum Chemical Methods.
- Searching molecular structure databases using tandem MS data: are we there yet?
- GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
- Low Data Drug Discovery with One-Shot Learning
- Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
- Model-based methods for continuous and discrete global optimization
- Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
- Bayesian molecular design with a chemical language model
- SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
- Learning More, with Less
- Molecular de-novo design through deep reinforcement learning
- DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction
- Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
- Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
- Quantum Information and Computation for Chemistry
- Convolutional neural networks for atomistic systems
- Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
- Sequence to Better Sequence: Continuous Revision of Combinatorial Structures
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