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
NATURE Fractal Based Analysis of the Influence of Odorants on Heart Activity. Hamidreza Namazi, Vladimir V. Kulish. Scientific Reports 6, Article number: 38555, DOI:10.1038/srep38555 (2016)
NATURE The Analysis of the Influence of Odorant’s Complexity on Fractal Dynamics of Human Respiration. Hamidreza Namazi, Amin Akrami, Vladimir V. Kulish. Scientific Reports 6, Article number: 26948, DOI:10.1038/srep26948 (2016)
NATURE Gold Nanoparticle Monolayers from Sequential Interfacial Ligand Exchange and Migration in a Three-Phase System. Guang Yang, T. Hallinan. Scientific Reports volume 6, Article number: 35339, DOI:10.1038/srep35339 (2016)
American Chemical Society (ACS) Extension of the SAFT-VR Mie EoS To Model Homonuclear Rings and Its Parametrization Based on the Principle of Corresponding States. Erich A. Müller, Andrés Mejía. Langmuir, 2017, 33 (42), pp 11518–11529, DOI: 10.1021/acs.langmuir.7b00976 (2017)
Royal Society of Chemistry (RSC) Nitrile-assistant eutectic electrolytes for cryogenic operation of lithium ion batteries at fast charges and discharges. Yoon-Gyo Cho, Young-Soo Kim, Dong-Gil Sung, Myung-Su Seo, Hyun-Kon Song. Energy Environ. Sci., 2014,7, 1737-1743 DOI: 10.1039/C3EE43029D (2014)
Hindawi Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal. Hamidreza Namazi, Amin Akrami, Sina Nazeri, Vladimir V. Kulish. BioMed Research International Volume 2016 Article ID 5469587, 5 pages doi:10.1155/2016/5469587 (2016)
Springer Electron-Transfer Secondary Reaction Matrices for MALDI MS Analysis of Bacteriochlorophyll a in Rhodobacter sphaeroides and Its Zinc and Copper Analogue Pigments. Calvano CD, Ventura G, Trotta M, Bianco G, Cataldi TR, Palmisano F. J Am Soc Mass Spectrom. 2017 Jan, 28(1), 125-135. DOI: 10.1007/s13361-016-1514-x (2017)
Springer A modified scaled variable reduced coordinate (SVRC)-quantitative structure property relationship (QSPR) model for predicting liquid viscosity of pure organic compounds. Seongmin Lee, Kiho Park, Yunkyung Kwon, Dae Ryook Yang. Korean Journal of Chemical Engineering, 2017, 34, 2715-2724, DOI: 10.1007/s11814-017-0173-3 (2017)
J-STAGE A Quantitative Structure-Property Relationship Model for Predicting the Critical Pressures of Organic Compounds Containing Oxygen, Sulfur, and Nitrogen. Ji Ye Oh, Kiho Park, Yangsoo Kim, Tae-Yun Park, Dae Ryook Yang. Journal of Chemical Engineering of Japan, Vol. 50, No. 6, pp. 1–11, 2017, DOI:10.1252/jcej.16we367 (2017)
American Chemical Society (ACS) Calculation of Average Molecular Parameters, Functional Groups, and a Surrogate Molecule for Heavy Fuel Oils Using 1H and 13C Nuclear Magnetic Resonance Spectroscopy. Abdul Gani Abdul Jameel, Ayman M. Elbaz, Abdul-Hamid Emwas, William L. Roberts, S. Mani Sarathy. Energy Fuels, 2016, 30 (5), pp 3894–3905, DOI: 10.1021/acs.energyfuels.6b00303 (2016)
Taylor & Francis Microbial growth yield estimates from thermodynamics and its importance for degradation of pesticides and formation of biogenic non-extractable residues. A. L. Brock, M. Kästner, S. Trapp. SAR and QSAR in Environmental Research, Volume 28, 2017, DOI: 10.1080/1062936X.2017.1365762 (2017)
Springer Many InChIs and quite some feat. Wendy A. Warr. Journal of Computer-Aided Molecular Design, 2015, Volume 29, Issue 8, pp 681–694, DOI: 10.1007/s10822-015-9854-3 (2015)
American Chemical Society (ACS) Comparative Study of the Ignition of 1-Decene, trans-5-Decene, and n-Decane: Constant-Volume Spray and Shock-Tube Experiments. Aniket Tekawade, Tianbo Xie, Matthew A. Oehlschlaeger. Energy Fuels, 2017, 31 (6), pp 6493–6500, DOI: 10.1021/acs. energyfuels.7b00430 (2017)
American Chemical Society (ACS) Computing the Diamagnetic Susceptibility and Diamagnetic Anisotropy of Membrane Proteins from Structural Subunits. Mahnoush Babaei, Isaac C. Jones, Kaushik Dayal, Meagan S. Mauter. J. Chem. Theory Comput., 2017, 13 (6), pp 2945–2953, DOI: 10.1021/ acs.jctc.6b01251 (2017)
Elsevier Spontaneous motion of various oil droplets in aqueous solution of trimethyl alkyl ammonium with different carbon chain lengths. Ben Nanzai, Megumi Kato, Manabu Igawa. Colloids and Surfaces A: Physicochemical and Engineering Aspects, Volume 504, 5 September 2016, Pages 154-160, DOI: 10.1016/j.colsurfa.2016.04.063 (2016)
Elsevier Electron scattering from C2-C8 symmetric ether molecules. Paresh Modak, Suvam Singh, Jaspreet Kaur, Bobby Antony. International Journal of Mass Spectrometry, 2016, Volume 409, Pages 1-8, DOI: 10.1016/j.ijms.2016.09.002 (2016)
Wiley A New Kaempferol-based Ru(II) Coordination Complex, Ru(kaem)Cl(DMSO)3: Structure and Absorption–Emission Spectroscopy Study. Mingwei Shao, Jongback Gang, Sanghyo Kim, Minyoung Yoon. Bull. Korean Chem. Soc., 2016, 37: 1625–1631. DOI: 10.1002/ bkcs.10916 (2016)
Springer Immune Network Technology on the Basis of Random Forest Algorithm for Computer-Aided Drug Design. Galina Samigulina, Samigulina Zarina. Bioinformatics and Biomedical Engineering, 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part I (2017)
ΣΥΝΔΕΣΜΟΣ ΕΛΛΗΝΙΚΩΝ ΑΚΑΔΗΜΑΪ ΚΩΝ ΒΙΒΛΙΟΘΗΚΩΝ Εργαστηριακές ασκή σεις κλινικής χημείας . Karkalousos, P., Zoi, G., Kroupis, C., Papaioannou, A., Plageras, P., Spyropoulos, V., Tsotsou, G., Fountzoula, C. 2015. [ebook] Athens:Hellenic Academic Libraries Link. Available Online at: http://hdl.handle.net/11419/5382
USPTO High solids content dendrimer polymer composition. Shaofeng WANG, Swee How Seow, Zeling Dou, Thomas F. Choate, Xiaoqun Ye. (NIPSEA TECHNOLOGIES PTE LTD, Singapore). US Patent US 2014/0142237 A1, May 22, 2014
Residue2Heat Thermo-physical characterization of FPBO and preliminary surrogate definition. A. Frassoldati, A Cuoci, A. Stagni, T. Faravelli, R. Calabria, P. Massoli. Project title: Renewable residential heating with fast pyrolysis bio-oil. Grant Agreement: 654650. Start of the project: 01.01.2016 (48 months)
ProQuest The development of guidance for solving polymer-penetrant diffusion problems in marine hardware. Rice, Matthew Aaron. Master Thesis. University of Rhode Island, ProQuest Dissertations Publishing, 2015.

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