Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?
出版年份 2017 全文链接
标题
Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?
作者
关键词
-
出版物
Scientific Reports
Volume 7, Issue 1, Pages -
出版商
Springer Nature
发表日期
2017-06-09
DOI
10.1038/s41598-017-02303-0
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Computer-Assisted Synthetic Planning: The End of the Beginning
- (2016) Sara Szymkuć et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Machine-learning-assisted materials discovery using failed experiments
- (2016) Paul Raccuglia et al. NATURE
- The Harvard organic photovoltaic dataset
- (2016) Steven A. Lopez et al. Scientific Data
- A Priori Estimation of Organic Reaction Yields
- (2015) Fateme S. Emami et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Development of a Novel Fingerprint for Chemical Reactions and Its Application to Large-Scale Reaction Classification and Similarity
- (2015) Nadine Schneider et al. Journal of Chemical Information and Modeling
- Economic reasoning and artificial intelligence
- (2015) D. C. Parkes et al. SCIENCE
- Machine learning: Trends, perspectives, and prospects
- (2015) M. I. Jordan et al. SCIENCE
- Regularization Paths for Generalized Linear Models via Coordinate Descent
- (2015) Jerome Friedman et al. Journal of Statistical Software
- Organic Chemistry as a Language and the Implications of Chemical Linguistics for Structural and Retrosynthetic Analyses
- (2014) Andrea Cadeddu et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Computer science: The learning machines
- (2014) Nicola Jones NATURE
- Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules
- (2013) Alessandro Lusci et al. Journal of Chemical Information and Modeling
- The big challenges of big data
- (2013) Vivien Marx NATURE
- Big (chemistry) data
- (2013) Bruce C. Gibb Nature Chemistry
- Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
- (2012) Geoffrey Hinton et al. IEEE SIGNAL PROCESSING MAGAZINE
- ReactionPredictor: Prediction of Complex Chemical Reactions at the Mechanistic Level Using Machine Learning
- (2012) Matthew A. Kayala et al. Journal of Chemical Information and Modeling
- Application of Molecular Topology for the Prediction of Reaction Yields and Anti-Inflammatory Activity of Heterocyclic Amidine Derivatives
- (2011) Jordi Pla-Franco et al. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
- Predicting a small molecule-kinase interaction map: A machine learning approach
- (2011) Fabian Buchwald et al. Journal of Cheminformatics
- Image processing and machine learning for fully automated probabilistic evaluation of medical images
- (2010) Luka Šajn et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Application of molecular topology for the prediction of the reaction times and yields under solvent-free conditions
- (2010) Jorge Gálvez et al. GREEN CHEMISTRY
- Ranking Chemical Structures for Drug Discovery: A New Machine Learning Approach
- (2010) Shivani Agarwal et al. Journal of Chemical Information and Modeling
- A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
- (2009) Bjoern H Menze et al. BMC BIOINFORMATICS
- Machine Learning for In Silico Virtual Screening and Chemical Genomics: New Strategies
- (2008) Jean-Philippe Vert et al. COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
- The future of biocuration
- (2008) Doug Howe et al. NATURE
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started