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Discover the perfect subscription plan for your needs by contacting us at contact@cc-dps.com.
Cited in Research Papers Like Nature
Based on 41 Patented Technologies
Awarded Korea Best Patents for 2014
The World's Largest in terms of Information Volume
Developed NAVER Chemical Structure Dictionary
World’s First chemical database based on quantum chemistry.
Over 2,100 sets of data are available for each and every 5+ million compounds.
The level of prediction accuracy by Mol-Instincts has been verified to be above 95% in most cases when compared with experimental data available to date
(other existing methods, e.g., Joback method provides 63% of the accuracy level for boiling point prediction)
Physical properties of chemical compounds are usually determined by experimental methods, which are time-consuming and costly to perform. In addition, in many cases experimental techniques are impossible to do due to the impurity, toxicity, and instability of chemicals.
Mol-Instincts has been developed based on 41 patented technologies, combining quantum chemistry, fundamental scientific methods, statistical thermodynamics, QSPR, SVRC, and ANN with a proprietary over-fitting prevention algorithm. Thanks to the automatized Mol-Instincts technologies, chemical properties can be analyzed within 10 hours while having the advantage of being affordable costing under two dollars.
Fundamental Scientific Approaches
Statistical Thermodynamics
QSPR (Quantitative Structure-Property Relationships)
SVRC (Scaled Variable Reduced Coordinates)
Over-fitting Prevented ANN (Artificial Neural Network)
Hydrocarbons | 958,000+ | |
---|---|---|
Nonhydrocarbons | Hetero Compounds | 1,510,000+ |
Halogen Compounds | 50,000+ | |
Extra-Hetero Compounds | 10,000+ | |
Drug-like Compounds | 1,312,000+ | |
Fuel Compounds | Gasoline | 105,000+ |
Jet-Fuel | 171,000+ | |
Diesel | 735,000+ | |
Biodiesel | 672,000+ | |
Chemical Processes | Soot Aromatic | 248,000+ |
Naphtha | 273,000+ | |
Combustion | 1,349,000+ | |
Thermal Cracking | 491,000+ | |
Catalytic Reforming | 408,000+ | |
Catalytic Cracking | 798,000+ | |
Hydro Cracking | 768,000+ | |
Desulfurization | 1,012,000+ | |
Isomerization | 231,000+ | |
GTL (Gas-To-Liquid) | 858,000+ | |
CTL (Coal-To-Liquid) | 1,249,000+ | |
MTO(Methanol-To-Olefin)/ MTG(Methanol-To-Gasoline) |
689,000+ |
Molinstincts Information & Applications
Mol-Instincts Usage Guide
Due to the limitations of experiments, hundreds of estimation methods and software have been developed. These are, however, mostly based on empirical approaches whose accuracy is low.
The predicted data were compared and verified with millions of experimental data collected from every possible source, including journal, textbooks, and existing databases for more than 5 years.
For the same example of normal boiling point estimation with 2,171 compounds, Mol-Instincts shows an accuracy level of above 95% while well-known existing Joback method shows accuracy of 63.07%
40 Related Patents
Input structure for the quantum calculation was determined by conformer analysis - where the most stable structure was used.
QSPR modeling was performed with more than 2,000 molecular descriptors obtained from quantum chemical calculations.
Predicted data were compared and verified with the experimental data available to date, where the accuracy level of 95% was confirmed in most cases.
The Mol-Instincts database containing over 2,100 sets of data and information per compound was constructed.
PUBLISHER | PUBLICATION |
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2013.05.20 | Multiple Linear Regression―Artificial Neural Network Model Predicting Ideal Gas Absolute Entropy of Pure Organic Compound for Normal State |
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2013.10.29 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Acentric Factor of Pure Organic Compound |
2013.10.29 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Critical Pressure of Pure Organic Compound |
2013.10.29 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Critical Temperature of Pure Organic Compound |
2013.10.29 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Critical Volume of Pure Organic Compound |
2013.10.29 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Energesis of Ideal Gas of Pure Organic Compound |
2013.10.29 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Enthalpy of Fusion at Melting Point of Pure Organic Compound |
2013.10.29 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Saturated Liquid Density of Pure rganic Compound at 298.15K |
2013.09.24 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Normal Boiling Point of Pure Organic Compound |
2013.09.24 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Refractive Index of Pure Organic Compound |
2013.05.20 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Solubility Index of Organic Compound |
2013.05.20 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Standard State Absolute Entropy of Pure Organic Compound |
2013.05.20 | Multiple Linear Regression―Artificial Neural Network Model Predicting Standard State Enthalpy of Formation of Pure Organic Compound |
2013.07.18 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Magnetic Susceptibility of Pure Organic Compound |
2013.08.21 | Multiple Linear Regression―Artificial Neural Network Model Predicting Polarizability of Pure Organic Compound |
2013.05.20 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Ionizing Energy of Pure Organic Compound |
2013.07.18 | Multiple Linear Regression Model Predicting Electron Affinity of Pure Organic Compound |
2013.08.09 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Parachor of Pure Organic Compound |
2013.08.21 | Multiple Linear Regression―Artificial Neural Network Model Predicting Flash Point of Pure Organic Compound |
2013.05.20 | Multiple Linear Regression- Artificial Neural Network Hybrid Model Predicting Lower Flammability Limit Temperature of Pure Organic Compound |
2013.08.06 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Lower Flammability Limit Volume Percent of Organic Compound |
2013.09.24 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Upper Flammability Limit Temperature of Organic Compound |
2013.08.21 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Upper Flammability Limit Volume Percent of Pure Organic Compound |
2013.05.20 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Liquid Density of Pure Organic Compound for Normal Boiling Point |
2013.09.24 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Heat of Vaporization of Pure Organic Compound for 298K |
2013.09.24 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Heat of Vaporization of Pure Organic Compound at Normal Boiling Point |
2013.08.06 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Octanol-Water Partition Coefficient of Pure Organic Compound |
2013.05.20 | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Water Solubility of Pure Organic Compound |
2013.04.23 | Multiple Linear Regression―Artificial Neural Network Hybrid Model Predicting Heat Capacity of Ideal Gas of Organic Compound |
2013.10.29 | SVRC Model Predicting Heat Capacity of Liquid of Pure Organic Compound |
2013.05.20 | SVRC Model Predicting Evaporation Heat of Pure Organic Compound |
2013.05.20 | SVRC Model Predicting Saturated Liquid Density of Pure Organic Compound |
2013.10.29 | QSPR Model Predicting Surface Tension of Liquid of Pure Organic Compound |
2013.08.27 | SVRC Model Predicting Thermal Conductivity of Liquid of Pure Organic Compound |
2013.08.06 | SVRC Model Predicting Thermal Conductivity of Gas of Pure Organic Compound |
2013.04.23 | SVRC Model Predicting Vapor Pressure of Liquid of Pure Organic Compound |
2013.09.24 | SVRC Model Predicting Liquid Viscosity of Pure Organic |
2013.09.24 | SVRC Model Predicting Gas Viscosity of Pure Organic |
2013.05.20 | Mathematical Model Predicting Second Virial Coefficient of Pure Organic Compound Through Boyle Temperature Prediction |
2013.05.02 | Automatic Method Using Quantum Mechanics Calculation Program and Materials Property Predictive Module and System therefor |
2014.03.12 | Method for Predicting a Property of Compound and System for Predicting a Property of Compound |