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About

First Chemical Database Based on Quantum Mechanics

2.85 million chemical compounds
Database for chemical property information

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

What is Mol-Instincts?

  • NEW

    A New Chemical
    Database

    World’s First chemical database based on quantum mechanics.

  • 2.85Million

    More than 56 Billion
    Sets of Data and Info

    Over 2,100 sets of data are available for each and every 2.85 million compounds.

  • 95%

    High Level of Accuracy

    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)

Product Summary

World’s First Database Based on Quantum Mechanics and QSPR with 41 Related Patented Technologies.

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 mechanics, 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.

  1. Quantum Mechanical Calculation, QSPR Molecular Descriptors

  2. 40 Patented Technologies

    Fundamental Scientific Approaches
    Statistical Thermodynamics
    QSPR (Quantitative Structure-Property Relationships)
    SVRC (Scaled Variable Reduced Coordinates)
    Over-fitting Prevented ANN (Artificial Neural Network)

  3. Quality Inspection of Predicted Data

  4. 1 Patented Technology

Exclusive Compounds

Number of Chemical Compounds Available
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+

Categories & Applications

Molinstincts Information & Applications

  • Thermo-Physicochemical Properties

    • Reaction engineering
    • Chemical process design / simulation / optimization
    • Energy efficiency improvement for combustion processes
    • Chemical safety and regulation
  • Quantum Data

    • Optimized 3D molecular structure
    • Energy level comparison among other molecules
    • Speed up molecular optimization by starting from
          the Mol-Instincts 3D structure
  • Molecular Descriptors

    • Obtaining descriptor values without running software
    • QSPR / QSAR modeling
  • Drug-Related Properties

    • New drug discovery
    • Drug possibility provision
  • Spectra Data

    • Application study with IR / NMR / VCD
  • Analysis Data

    • Obtaining optimized molecular structure (2D/3D)
    • Vibrational frequency analysis & animation
    • Molecular orbitals (HOMO, LUMO)

How to Use

Mol-Instincts Usage Guide

  1. Access Mol-Instincts Search website.

    https://search.molinstincts.com
  2. Search your compounds by Text / Structure / Property.

    Search a Compound
  3. Click 'View our data' of the matching compound in the result list to move to the property view page.

    View our data
  4. Similar compounds are also shown along with matching accuracy.

    Results with Matching Accuracy
  5. Seven different property categories are available – simply select as needed.

    View Chemical Properties

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%

Existing technology prediction accuracy sample Existing technology prediction accuracy distribution sample

Accuracy of Mol-Instincts Prediction
95.02%

Chemessen technology prediction accuracy sample Chemessen technology prediction accuracy distribution sample

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

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