A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
Authors
Keywords
-
Journal
Journal of Cheminformatics
Volume 12, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-02-21
DOI
10.1186/s13321-020-0414-z
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- DeepChemStable: Chemical Stability Prediction with an Attention-Based Graph Convolution Network
- (2019) Xiuming Li et al. Journal of Chemical Information and Modeling
- Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction
- (2019) Ke Liu et al. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
- Analyzing Learned Molecular Representations for Property Prediction
- (2019) Kevin Yang et al. Journal of Chemical Information and Modeling
- Molecule Property Prediction Based on Spatial Graph Embedding
- (2019) Xiaofeng Wang et al. Journal of Chemical Information and Modeling
- The rise of deep learning in drug discovery
- (2018) Hongming Chen et al. DRUG DISCOVERY TODAY
- MoleculeNet: a benchmark for molecular machine learning
- (2018) Zhenqin Wu et al. Chemical Science
- Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future
- (2017) Pavel Polishchuk Journal of Chemical Information and Modeling
- Molecular de-novo design through deep reinforcement learning
- (2017) Marcus Olivecrona et al. Journal of Cheminformatics
- Molecular graph convolutions: moving beyond fingerprints
- (2016) Steven Kearnes et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
- (2016) Rafael Gómez-Bombarelli et al. NATURE MATERIALS
- Machine Learning Strategy for Accelerated Design of Polymer Dielectrics
- (2016) Arun Mannodi-Kanakkithodi et al. Scientific Reports
- Mining Discriminative Patterns from Graph Data with Multiple Labels and Its Application to Quantitative Structure–Activity Relationship (QSAR) Models
- (2015) Zheng Shao et al. Journal of Chemical Information and Modeling
- Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
- (2015) Junshui Ma et al. Journal of Chemical Information and Modeling
- Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
- (2015) Katja Hansen et al. Journal of Physical Chemistry Letters
- QSAR Modeling: Where Have You Been? Where Are You Going To?
- (2013) Artem Cherkasov et al. JOURNAL OF MEDICINAL CHEMISTRY
- Open-source platform to benchmark fingerprints for ligand-based virtual screening
- (2013) Sereina Riniker et al. Journal of Cheminformatics
- Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
- (2012) Tu Le et al. CHEMICAL REVIEWS
- The influence of lipophilicity in drug discovery and design
- (2012) John A Arnott et al. Expert Opinion on Drug Discovery
- Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information
- (2011) Iurii Sushko et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Extended-Connectivity Fingerprints
- (2010) David Rogers et al. Journal of Chemical Information and Modeling
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started