Open-source QSAR models for pKa prediction using multiple machine learning approaches
暂无分享,去创建一个
Antony J. Williams | Neal F. Cariello | Alexandru Korotcov | Kamel Mansouri | Valery Tkachenko | Chris M. Grulke | Catherine S. Sprankle | David Allen | Warren M. Casey | Nicole C. Kleinstreuer | Antony J. Williams | C. Grulke | K. Mansouri | W. Casey | A. Korotcov | N. Kleinstreuer | Valery Tkachenko | C. Sprankle | D. Allen | Neal F. Cariello
[1] Harvey J. Clewell,et al. High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling. , 2018, The Science of the total environment.
[2] acid dissociation constant , 2009 .
[3] Kamel Mansouri,et al. A comparison of three liquid chromatography (LC) retention time prediction models. , 2018, Talanta.
[4] L. Di,et al. Physicochemical profiling: overview of the screens. , 2004, Drug discovery today. Technologies.
[5] Hilde van der Togt,et al. Publisher's Note , 2003, J. Netw. Comput. Appl..
[6] Davide Ballabio,et al. Evaluation of model predictive ability by external validation techniques , 2010 .
[7] Antony J. Williams,et al. ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology. , 2016, Chemical research in toxicology.
[8] Igor V. Tetko,et al. Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM) , 2013, Journal of Cheminformatics.
[9] Davide Castelvecchi,et al. Can we open the black box of AI? , 2016, Nature.
[10] Alex Zhavoronkov,et al. Applications of Deep Learning in Biomedicine. , 2016, Molecular pharmaceutics.
[11] Antony J. Williams,et al. OPERA models for predicting physicochemical properties and environmental fate endpoints , 2018, Journal of Cheminformatics.
[12] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[13] Robert G. Pearce,et al. Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics , 2018, Toxicological sciences : an official journal of the Society of Toxicology.
[14] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[15] O. Stegle,et al. Deep learning for computational biology , 2016, Molecular systems biology.
[16] Thomas Sander,et al. DataWarrior: An Open-Source Program For Chemistry Aware Data Visualization And Analysis , 2015, J. Chem. Inf. Model..
[17] Max Kuhn,et al. Building Predictive Models in R Using the caret Package , 2008 .
[18] Marc C. Nicklaus,et al. Comparison of Nine Programs Predicting pKa Values of Pharmaceutical Substances , 2009, J. Chem. Inf. Model..
[19] Andy Liaw,et al. Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships , 2016, J. Chem. Inf. Model..
[20] D. Manallack,et al. Drug Targeting of α-Synuclein Oligomerization in Synucleinopathies , 2007 .
[21] Leopold Parts,et al. Computational biology: deep learning , 2017, Emerging topics in life sciences.
[22] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[23] R. M. Muir,et al. Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients , 1962, Nature.
[24] CHUN WEI YAP,et al. PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints , 2011, J. Comput. Chem..
[25] Thorsten Meinl,et al. KNIME: The Konstanz Information Miner , 2007, GfKl.
[26] I. Tetko,et al. Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database. , 2004, Journal of pharmaceutical sciences.
[27] R. Leardi,et al. Genetic algorithms applied to feature selection in PLS regression: how and when to use them , 1998 .
[28] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[29] Imran Shah,et al. Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure. , 2017, Chemical research in toxicology.
[30] R. Frische,et al. Physicochemical properties as useful tools for predicting the environmental fate of organic chemicals. , 1982, Ecotoxicology and environmental safety.
[31] Igor V. Tetko,et al. Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information , 2011, J. Comput. Aided Mol. Des..
[32] Gordon M. Crippen,et al. Predicting p K a . , 2009 .
[33] Melvin E. Andersen,et al. Incorporating High-Throughput Exposure Predictions With Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing , 2015, Toxicological sciences : an official journal of the Society of Toxicology.
[34] Robert P. Sheridan,et al. Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships , 2015, J. Chem. Inf. Model..
[35] I. Tetko,et al. Predicting the pKa of Small Molecules , 2011 .
[36] Gordon M. Crippen,et al. Predicting pKa , 2009, J. Chem. Inf. Model..
[37] Paul Voosen,et al. How AI detectives are cracking open the black box of deep learning , 2017 .
[38] Budget,et al. Memorandum for the Heads of Executive Departments and Agencies: Open Data Policy--Managing Information as an Asset , 2013 .
[39] B. Obama. Executive Order 13642: Making Open and Machine Readable the New Default for Government Information , 2013 .
[40] Abhinav Vishnu,et al. Deep learning for computational chemistry , 2017, J. Comput. Chem..
[41] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[42] François Chollet,et al. Keras: The Python Deep Learning library , 2018 .
[43] Roberto Todeschini,et al. Beware of Unreliable Q2! A Comparative Study of Regression Metrics for Predictivity Assessment of QSAR Models , 2016, J. Chem. Inf. Model..
[44] S. Joshua Swamidass,et al. Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism. , 2017, Chemical research in toxicology.
[45] Alexander Tropsha,et al. Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation , 2016, J. Chem. Inf. Model..
[46] A M Richard,et al. An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling$ , 2016, SAR and QSAR in environmental research.
[47] Loriano Storchi,et al. In silico pKa Prediction and ADME Profiling , 2009, Chemistry & biodiversity.
[48] Division on Earth,et al. A Framework to Guide Selection of Chemical Alternatives , 2014 .
[49] Roberto Todeschini,et al. Comparison of Different Approaches to Define the Applicability Domain of QSAR Models , 2012, Molecules.
[50] Ruili Huang,et al. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project , 2016, Environmental health perspectives.
[51] Mahdi Vasighi,et al. Genetic Algorithms for architecture optimisation of Counter-Propagation Artificial Neural Networks , 2011 .
[52] Johann Gasteiger,et al. New Publicly Available Chemical Query Language, CSRML, To Support Chemotype Representations for Application to Data Mining and Modeling , 2015, J. Chem. Inf. Model..