BioCompoundML: A General Biofuel Property Screening Tool for Biological Molecules Using Random Forest Classifiers

Whitmore, Leanne S.; Davis, Ryan W.; McCormick, Robert L.; Gladden, John M.; Simmons, Blake A.; George, Anthe; Hudson, Corey M.

doi:10.1021/acs.energyfuels.6b01952

BioCompoundML: A General Biofuel Property Screening Tool for Biological Molecules Using Random Forest Classifiers

Journal Article · Thu Sep 15 00:00:00 EDT 2016 · Energy and Fuels

DOI:https://doi.org/10.1021/acs.energyfuels.6b01952· OSTI ID:1327434

Whitmore, Leanne S. ^[1]; Davis, Ryan W. ^[2]; McCormick, Robert L. ^[3]; Gladden, John M. ^[1]; Simmons, Blake A. ^[4]; George, Anthe ^[1]; Hudson, Corey M. ^[1]

Sandia National Lab. (SNL-CA), Livermore, CA (United States); Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States)
Sandia National Laboratories, Livermore, California 94551, United States
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Not Available

View Journal Article

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

Grant/Contract Number:: AC36-08GO28308; AC04-94AL85000; AC02-05CH11231

OSTI ID:: 1327434

Alternate ID(s):: OSTI ID: 1332664
OSTI ID: 1529804
OSTI ID: 1440943

Journal Information:: Energy and Fuels, Journal Name: Energy and Fuels Journal Issue: 10 Vol. 30; ISSN 0887-0624

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

References (31)

Current Modeling Methods Used in QSAR/QSPR Yee, Liew Chin; Wei, Yap Chun Statistical Modelling of Molecular Descriptors in QSAR/QSPR, Volume 2 https://doi.org/10.1002/9783527645121.ch1	book	January 2012
PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints Yap, Chun Wei Journal of Computational Chemistry, Vol. 32, Issue 7 https://doi.org/10.1002/jcc.21707	journal	December 2010
Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information Sushko, Iurii; Novotarskyi, Sergii; Körner, Robert Journal of Computer-Aided Molecular Design, Vol. 25, Issue 6 https://doi.org/10.1007/s10822-011-9440-2	journal	June 2011
Octane numbers (ONs) of hydrocarbons: a QSPR study using optimal topological indices for the topological equivalents of the ONs Smolenskii, E. A.; Ryzhov, A. N.; Bavykin, V. M. Russian Chemical Bulletin, Vol. 56, Issue 9 https://doi.org/10.1007/s11172-007-0262-2	journal	September 2007
Effect of molecular structure on incipient soot formation Calcote, H. F.; Manos, D. M. Combustion and Flame, Vol. 49, Issue 1-3 https://doi.org/10.1016/0010-2180(83)90172-4	journal	January 1983
The effects of molecular structure on soot formation II. Diffusion flames Olson, D. B.; Pickens, J. C.; Gill, R. J. Combustion and Flame, Vol. 62, Issue 1 https://doi.org/10.1016/0010-2180(85)90092-6	journal	October 1985
Automatic learning of chemical concepts: Research octane number and molecular substructures Blurock, Edward S. Computers & Chemistry, Vol. 19, Issue 2 https://doi.org/10.1016/0097-8485(95)00001-9	journal	June 1995
Mechanism of smoke formation in diffusion flames Schalla, Rose L.; McDonald, Glen E. Symposium (International) on Combustion, Vol. 5, Issue 1 https://doi.org/10.1016/S0082-0784(55)80042-2	journal	January 1955
A study of jet fuel sooting tendency using the threshold sooting index (TSI) model Yang, Yi; Boehman, André L.; Santoro, Robert J. Combustion and Flame, Vol. 149, Issue 1-2 https://doi.org/10.1016/j.combustflame.2006.11.007	journal	April 2007
Particulate matter indices using fuel smoke point for vehicle emissions with gasoline, ethanol blends, and butanol blends Barrientos, Eduardo J.; Anderson, James E.; Maricq, M. Matti Combustion and Flame, Vol. 167 https://doi.org/10.1016/j.combustflame.2016.01.034	journal	May 2016
Chemical product design: challenges and opportunities Gani, Rafiqul Computers & Chemical Engineering, Vol. 28, Issue 12 https://doi.org/10.1016/j.compchemeng.2004.08.010	journal	November 2004
Model-Based Design of Tailor-Made Biofuels Dahmen, Manuel; Marquardt, Wolfgang Energy & Fuels, Vol. 30, Issue 2 https://doi.org/10.1021/acs.energyfuels.5b02674	journal	February 2016
The Effect of Compression Ratio, Fuel Octane Rating, and Ethanol Content on Spark-Ignition Engine Efficiency Leone, Thomas G.; Anderson, James E.; Davis, Richard S. Environmental Science & Technology, Vol. 49, Issue 18 https://doi.org/10.1021/acs.est.5b01420	journal	July 2015
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling Svetnik, Vladimir; Liaw, Andy; Tong, Christopher Journal of Chemical Information and Computer Sciences, Vol. 43, Issue 6 https://doi.org/10.1021/ci034160g	journal	November 2003
When is Chemical Similarity Significant? The Statistical Distribution of Chemical Similarity Scores and Its Extreme Values Baldi, Pierre; Nasr, Ramzi Journal of Chemical Information and Modeling, Vol. 50, Issue 7 https://doi.org/10.1021/ci100010v	journal	June 2010
Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure Property Relationship (QSPR) Methods Saldana, Diego Alonso; Starck, Laurie; Mougin, Pascal Energy & Fuels, Vol. 25, Issue 9 https://doi.org/10.1021/ef200795j	journal	September 2011
Relation of Smoke Point to Molecular Structure Hunt, Russell A. Industrial & Engineering Chemistry, Vol. 45, Issue 3 https://doi.org/10.1021/ie50519a039	journal	March 1953
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Anthropogenic reaction parameters – the missing link between chemical intuition and the available chemical space Keserű, György M.; Soós, Tibor; Kappe, C. Oliver Chem. Soc. Rev., Vol. 43, Issue 15 https://doi.org/10.1039/C3CS60423C	journal	January 2014
Catalytic strategies for improving specific fuel properties Do, Phuong T. M.; Crossley, Steven; Santikunaporn, Malee Catalysis https://doi.org/10.1039/b602366p	book	January 2007
On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods Saldana, D. A.; Starck, L.; Mougin, P. SAR and QSAR in Environmental Research, Vol. 24, Issue 4 https://doi.org/10.1080/1062936X.2013.766634	journal	April 2013
An analysis of four missing data treatment methods for supervised learning Batista, Gustavo E. A. P. A.; Monard, Maria Carolina Applied Artificial Intelligence, Vol. 17, Issue 5-6 https://doi.org/10.1080/713827181	journal	May 2003
Missing value estimation methods for DNA microarrays Troyanskaya, O.; Cantor, M.; Sherlock, G. Bioinformatics, Vol. 17, Issue 6 https://doi.org/10.1093/bioinformatics/17.6.520	journal	June 2001
The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases Caspi, Ron; Altman, Tomer; Billington, Richard Nucleic Acids Research, Vol. 42, Issue D1 https://doi.org/10.1093/nar/gkt1103	journal	November 2013
PubChem Substance and Compound databases Kim, Sunghwan; Thiessen, Paul A.; Bolton, Evan E. Nucleic Acids Research, Vol. 44, Issue D1 https://doi.org/10.1093/nar/gkv951	journal	September 2015
Randomized Clustering Forests for Image Classification Moosmann, F.; Nowak, E.; Jurie, F. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, Issue 9 https://doi.org/10.1109/TPAMI.2007.70822	journal	September 2008
QSPR Models for Octane Number Prediction Al-Fahemi, Jabir H.; Albis, Nahla A.; Gad, Elshafie A. M. Journal of Theoretical Chemistry, Vol. 2014 https://doi.org/10.1155/2014/520652	journal	August 2014
Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules Murrell, Daniel S.; Cortes-Ciriano, Isidro; van Westen, Gerard J. P. Journal of Cheminformatics, Vol. 7, Issue 1 https://doi.org/10.1186/s13321-015-0086-2	journal	August 2015
Feature Selection with the Boruta Package Kursa, Miron B.; Rudnicki, Witold R. Journal of Statistical Software, Vol. 36, Issue 11 https://doi.org/10.18637/jss.v036.i11	journal	January 2010
Compendium of Experimental Cetane Numbers Yanowitz, J.; Ratcliff, M.; McCormick, R. https://doi.org/10.2172/1150177	report	August 2014
Jean-Claude Bradley Open Melting Point Dataset Bradley, Jean-Claude; Williams, Antony; Lang, Andrew figshare https://doi.org/10.6084/m9.figshare.1031637	dataset	January 2014

Similar Records

BioCompoundML: a general biofuel property screening tool for biological molecules using Random Forest Classifiers.

Conference · Mon Aug 01 00:00:00 EDT 2016 · OSTI ID:1529804

BioCompoundML: A General Biofuel Property Screening Tool for Biological Molecules Using Random Forest Classifiers

Citation Formats

References (31)

Similar Records

Related Subjects