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ACCURACY

Accuracy Level of Above 95%

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.

Accuracy of Existing Estimation Methods (Joback Method) 63.07%

Accuracy of Mol-Instincts Prediction 95.02%

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%

DEVELOPMENT PROCESS

40 Related Patents

  • 01

    High Quality
    Quantum Calculation

    Input structure for the quantum calculation was determined by conformer analysis - where the most stable structure was used.

  • 02

    Most Advanced
    QSPR Modeling

    QSPR modeling was performed with more than 2,000 molecular descriptors obtained from QM calculations.

  • 03

    Detailed Model
    Verification

    Predicted data were compared and verified with the experimental data available to date, where the accuracy level of 95% was confirmed in most cases.

  • 04

    Chemical Property
    Categorization

    The Mol-Instincts database containing over 2,100 sets of data and information per compound was constructed.

CITATION LIST

Cited in authoritative journals such as Nature

Below is a partial list of collected citations.
PUBLISHER PUBLICATION

PATENTS LIST

2013.05.20 Multiple Linear Regression―Artificial Neural Network Model Predicting Ideal Gas Absolute Entropy of Pure Organic Compound for Normal State
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