Odor prediction and aroma mixture design using machine learning model and molecular surface charge density profiles

[1]  Cheng Wang,et al.  Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction. , 2021, Journal of the American Chemical Society.

[2]  Rafiqul Gani,et al.  Chemical product design – recent advances and perspectives , 2020 .

[3]  Rafiqul Gani,et al.  Systematic Model-Based Methodology for Substitution of Hazardous Chemicals , 2019, ACS Sustainable Chemistry & Engineering.

[4]  R. Rengaswamy,et al.  Machine Learning Derived Quantitative Structure Property Relationship (QSPR) to Predict Drug Solubility in Binary Solvent Systems , 2019, Industrial & Engineering Chemistry Research.

[5]  Zhigang Lei,et al.  COSMO‐UNIFAC model for ionic liquids , 2019, AIChE Journal.

[6]  Xinyan Liu,et al.  Heat Capacity Prediction of Ionic Liquids Based on Quantum Chemistry Descriptors , 2018, Industrial & Engineering Chemistry Research.

[7]  William H. Green,et al.  Using Machine Learning To Predict Suitable Conditions for Organic Reactions , 2018, ACS central science.

[8]  Zheng Li,et al.  Toward artificial intelligence in catalysis , 2018, Nature Catalysis.

[9]  Daniel W. Davies,et al.  Machine learning for molecular and materials science , 2018, Nature.

[10]  Lei Zhang,et al.  A machine learning based computer-aided molecular design/screening methodology for fragrance molecules , 2018, Comput. Chem. Eng..

[11]  G. Kupgan,et al.  Modeling Amorphous Microporous Polymers for CO2 Capture and Separations. , 2018, Chemical reviews.

[12]  Yuanfang Guan,et al.  Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features , 2017, GigaScience.

[13]  Nikolaos V. Sahinidis,et al.  COSMO‐based computer‐aided molecular/mixture design: A focus on reaction solvents , 2018 .

[14]  K. Hayashi,et al.  Machine-Learning-Based Olfactometer: Prediction of Odor Perception from Physicochemical Features of Odorant Molecules. , 2017, Analytical chemistry.

[15]  Zhijun Zhao,et al.  Predicting the Viscosity of Ionic Liquids by the ELM Intelligence Algorithm , 2017 .

[16]  Rafiqul Gani,et al.  New Vistas in Chemical Product and Process Design. , 2016, Annual review of chemical and biomolecular engineering.

[17]  Andreas Keller,et al.  Olfactory perception of chemically diverse molecules , 2016, BMC Neuroscience.

[18]  Bastian Schmid,et al.  Group Contribution Methods for Phase Equilibrium Calculations. , 2015, Annual review of chemical and biomolecular engineering.

[19]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[20]  Kai Sundmacher,et al.  Integrated solvent and process design exemplified for a Diels–Alder reaction , 2014 .

[21]  Alírio E. Rodrigues,et al.  Prediction Model for the Odor Intensity of Fragrance Mixtures: A Valuable Tool for Perfumed Product Design , 2013 .

[22]  Andreas Klamt,et al.  COSMOsar3D: Molecular Field Analysis Based on Local COSMO σ-Profiles , 2012, J. Chem. Inf. Model..

[23]  Andreas Klamt,et al.  COSMOsim3D: 3D-Similarity and Alignment Based on COSMO Polarization Charge Densities , 2012, J. Chem. Inf. Model..

[24]  Andreas Klamt,et al.  Polarization charge densities provide a predictive quantification of hydrogen bond energies. , 2012, Physical chemistry chemical physics : PCCP.

[25]  Albert-László Barabási,et al.  Flavor network and the principles of food pairing , 2011, Scientific reports.

[26]  Y. A. Liu,et al.  Sigma Profile Database for Predicting Solid Solubility in Pure and Mixed Solvent Mixtures for Organic Pharmacological Compounds with COSMO-Based Thermodynamic Methods , 2008 .

[27]  Wendy Wolfson.  In the fragrance business, the right molecule smells like money. , 2005, Chemistry & biology.

[28]  Rafiqul Gani,et al.  A New Decomposition-Based Computer-Aided Molecular/Mixture Design Methodology for the Design of Optimal Solvents and Solvent Mixtures , 2005 .

[29]  Lawrence C. Katz,et al.  Encoding social signals in the mouse main olfactory bulb , 2005, Nature.

[30]  B. Lavine.  Electronic van der Waals Surface Property Descriptors and Genetic Algorithms for Developing Structure-Activity Correlations in Olfactory Databases , 2003, J. Chem. Inf. Model..

[31]  Stanley I. Sandler,et al.  A Priori Phase Equilibrium Prediction from a Segment Contribution Solvation Model , 2002 .

[32]  Jorge A. Marrero,et al.  Group-contribution based estimation of pure component properties , 2001 .

[33]  C. Breneman,et al.  QSPR analysis of HPLC column capacity factors for a set of high‐energy materials using electronic van der waals surface property descriptors computed by transferable atom equivalent method , 1997 .

[34]  Karen J. Rossiter,et al.  Structure−Odor Relationships , 1996 .

[35]  Sandro Macchietto,et al.  Computer aided molecular design: a novel method for optimal solvent selection , 1993 .

[36]  A. Klamt,et al.  COSMO : a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient , 1993 .

[37]  Kevin G Joback,et al.  Designing molecules possessing desired physical property values , 1989 .

[38]  C. Ham,et al.  Structure-activity studies of musk odorants using pattern recognition: monocyclic nitrobenzenes , 1985 .

[39]  Rafiqul Gani,et al.  MOLECULAR DESIGN OF SOLVENTS FOR LIQUID EXTRACTION BASED ON UNIFAC , 1983 .

[40]  M. Chastrette,et al.  Discrimination of camphoraceous substances using physicochemical parameters , 1983 .

[41]  Peter C. Jurs,et al.  Computer-Assisted Studies of Chemical Structure and Olfactory Quality Using Pattern Recognition Techniques , 1981 .

[42]  M. Chastrette.  An approach to a classification of odours using physicochemical parameters , 1981 .