Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues
Published 2017 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues
Authors
Keywords
-
Journal
RSC Advances
Volume 7, Issue 31, Pages 19007-19018
Publisher
Royal Society of Chemistry (RSC)
Online
2017-03-29
DOI
10.1039/c6ra28442f
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- The model adaptive space shrinkage (MASS) approach: a new method for simultaneous variable selection and outlier detection based on model population analysis
- (2016) Ming Wen et al. ANALYST
- Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches
- (2016) Nikita Basant et al. COMPUTATIONAL BIOLOGY AND CHEMISTRY
- ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting
- (2016) Ning-Ning Wang et al. Journal of Chemical Information and Modeling
- TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models
- (2016) Zhi-Jiang Yao et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions
- (2016) Jie Dong et al. Journal of Cheminformatics
- In vitro prediction of human intestinal absorption and blood–brain barrier partitioning: development of a lipid analog for micellar liquid chromatography
- (2015) Mike De Vrieze et al. ANALYTICAL AND BIOANALYTICAL CHEMISTRY
- In silico toxicity prediction of chemicals from EPA toxicity database by kernel fusion-based support vector machines
- (2015) Dong-Sheng Cao et al. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
- Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption
- (2015) Danielle Newby et al. EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
- In silicoevaluation of logD7.4and comparison with other prediction methods
- (2015) Jian-Bing Wang et al. JOURNAL OF CHEMOMETRICS
- Computational Investigation of Antifungal Compounds Using Molecular Modeling and Prediction of ADME/Tox Properties
- (2015) Edilson L. Cunha et al. Journal of Computational and Theoretical Nanoscience
- ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation
- (2015) Jie Dong et al. Journal of Cheminformatics
- Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
- (2012) Christopher A. Lipinski et al. ADVANCED DRUG DELIVERY REVIEWS
- The impact of training set data distributions for modelling of passive intestinal absorption
- (2012) Taravat Ghafourian et al. INTERNATIONAL JOURNAL OF PHARMACEUTICS
- Computer-aided prediction of toxicity with substructure pattern and random forest
- (2012) Dong-Sheng Cao et al. JOURNAL OF CHEMOMETRICS
- ADME Evaluation in Drug Discovery. 9. Prediction of Oral Bioavailability in Humans Based on Molecular Properties and Structural Fingerprints
- (2011) Sheng Tian et al. MOLECULAR PHARMACEUTICS
- Prediction of drug intestinal absorption by new linear and non-linear QSPR
- (2010) Alan Talevi et al. EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
- Estimation of ADME Properties with Substructure Pattern Recognition
- (2010) Jie Shen et al. Journal of Chemical Information and Modeling
- Combinatorial QSAR Modeling of Human Intestinal Absorption
- (2010) Claudia Suenderhauf et al. MOLECULAR PHARMACEUTICS
- Neural computational prediction of oral drug absorption based on CODES 2D descriptors
- (2009) A. Guerra et al. EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
- Prediction of Human Intestinal Absorption by GA Feature Selection and Support Vector Machine Regression
- (2008) Aixia Yan et al. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More