A de novo molecular generation method using latent vector based generative adversarial network
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A de novo molecular generation method using latent vector based generative adversarial network
Authors
Keywords
-
Journal
Journal of Cheminformatics
Volume 11, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2019-12-03
DOI
10.1186/s13321-019-0397-9
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Exploring the GDB-13 chemical space using deep generative models
- (2019) Josep Arús-Pous et al. Journal of Cheminformatics
- Randomized SMILES strings improve the quality of molecular generative models
- (2019) Josep Arús-Pous et al. Journal of Cheminformatics
- Machine learning in chemoinformatics and drug discovery
- (2018) Yu-Chen Lo et al. DRUG DISCOVERY TODAY
- The rise of deep learning in drug discovery
- (2018) Hongming Chen et al. DRUG DISCOVERY TODAY
- Reinforced Adversarial Neural Computer for de Novo Molecular Design
- (2018) Evgeny Putin et al. Journal of Chemical Information and Modeling
- Cheminformatics in Drug Discovery, an Industrial Perspective
- (2018) Hongming Chen et al. Molecular Informatics
- Adversarial Threshold Neural Computer for Molecular de Novo Design
- (2018) Evgeny Putin et al. MOLECULAR PHARMACEUTICS
- Molecular generative model based on conditional variational autoencoder for de novo molecular design
- (2018) Jaechang Lim et al. Journal of Cheminformatics
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
- (2018) Rafael Gómez-Bombarelli et al. ACS Central Science
- Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
- (2018) Kristina Preuer et al. Journal of Chemical Information and Modeling
- Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
- (2018) Daniil Polykovskiy et al. MOLECULAR PHARMACEUTICS
- Multi-objective de novo drug design with conditional graph generative model
- (2018) Yibo Li et al. Journal of Cheminformatics
- Artificial Intelligence in Drug Design
- (2018) Gerhard Hessler et al. MOLECULES
- Application of Generative Autoencoder in De Novo Molecular Design
- (2017) Thomas Blaschke et al. Molecular Informatics
- Erratum to: ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics
- (2017) Jiangming Sun et al. Journal of Cheminformatics
- Molecular de-novo design through deep reinforcement learning
- (2017) Marcus Olivecrona et al. Journal of Cheminformatics
- Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
- (2017) Marwin H. S. Segler et al. ACS Central Science
- De Novo Design at the Edge of Chaos
- (2016) Petra Schneider et al. JOURNAL OF MEDICINAL CHEMISTRY
- Hybrid computing using a neural network with dynamic external memory
- (2016) Alex Graves et al. NATURE
- The ChEMBL database in 2017
- (2016) Anna Gaulton et al. NUCLEIC ACIDS RESEARCH
- The Next Era: Deep Learning in Pharmaceutical Research
- (2016) Sean Ekins PHARMACEUTICAL RESEARCH
- The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
- (2016) Artur Kadurin et al. Oncotarget
- Deep Learning in Drug Discovery
- (2015) Erik Gawehn et al. Molecular Informatics
- Quantifying the chemical beauty of drugs
- (2012) G. Richard Bickerton et al. Nature Chemistry
- Reaction-drivende novodesign, synthesis and testing of potential type II kinase inhibitors
- (2011) Gisbert Schneider et al. Future Medicinal Chemistry
- Classification of Organic Molecules by Molecular Quantum Numbers
- (2009) Kong T. Nguyen et al. ChemMedChem
- Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
- (2009) Peter Ertl et al. Journal of Cheminformatics
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now