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Mol-Instincts
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.
Quantum Mechanical Calculation,
QSPR Molecular Descriptors
40 Patented
Technologies
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)
Quality Inspection of
Predicted Data
1 Patented
Technologies
Chemical Database & Online Service
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+ |
| Naphta | 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)
Mol-Instincts 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%
(Mol-Instincts)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
High Quality
Quantum Calculation
Input structure for the quantum calculation was determined by conformer analysis – where the most stable structure was used.
Most Advanced
QSPR Modeling
QSPR modeling was performed with more than 2,000 molecular descriptors obtained from QM calculations.
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.
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 Inflence of Odorants on Heart Activity. Hamidreza Namazi, Vladimir V. Kulish. Scientifi Reports 6, Article number: 38555, DOI:10.1038/srep38555 (2016) |
| NATURE | The Analysis of the Inflence of Odorant’s Complexity on Fractal Dynamics of Human Respiration. Hamidreza Namazi, Amin Akrami,Vladimir V. Kulish. Scientifi Reports 6, Article number: 26948, DOI:10.1038/srep26948 (2016) |
| MDPI | Free Accessible Databases as a Source of Information about Food Components and Other Compounds with Anticancer Activity–Brief Review. Piotr Minkiewicz, Marta Turło, Anna Iwaniak and Małgorzata Darewicz. Molecules 2019, 24(4), 789, DOI: 10.3390/molecules24040789 (2019) |
| 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) |
| 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) |
| Springer | The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. Antony J. Williams, Christopher M. Grulke, Jeff Edwards, Andrew D. McEachran, Kamel Mansouri, Nancy C. Baker, Grace Patlewicz, Imran Shah, John F. Wambaugh, Richard S. Judson, Ann M. Richard. J Cheminform (2017) 9:61, DOI: 10.1186/s13321-017-0247-6 (2017) |
| 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) |
| Residue2Heat | THERMO-PHYSICAL CHARACTERIZATION OF FPBO AND PRELIMINARY SURROGATE DEFINITION. Project title: Renewable residential heating with fast pyrolysis bio-oil. A. Frassoldati, A Cuoci, A. Stagni, T. Faravelli, R. Calabria, P. Massoli. Grant Agreement: 654650. Start of the project: 01.01.2016 (48 months) |
| NATURE | Gold Nanoparticle Monolayers from Sequential Interfacial Ligand Exchange and Migration in a Three-Phase System. Guang Yang, T.Hallinan. Scientifi Reports volume 6, Article number: 35339, DOI:10.1038/srep35339 (2016) |
| American Chemical Society (ACS) | 1,5-Diaminonaphtalene is a Highly Performing Electron-Transfer Secondary-Reaction Matrix for Laser Desorption Ionization Mass Spectrometry of Indolenine-Based Croconaines. Cosima D. Calvano, Maria Annunziata M. Capozzi , Angela Punzi , Gianluca M. Farinola , Tommaso R. I. Cataldi , and Francesco Palmisano. ACS Omega, 2018, 3 (12), pp 17821–17827, DOI: 10.1021/acsomega.8b02575 (2018) |
| American Chemical Society (ACS) | Propylphenol to Phenol and Propylene over Acidic Zeolites: Role of Shape Selectivity and Presence of Steam. Yuhe Liao, Ruyi Zhong, Ekaterina Makshina, Martin d’Halluin, Yannick van Limbergen, Danny Verboekend, and Bert F. Sels. ACS Catal. 2018, 8, 7861-7878, DOI:10.1021/acscatal.8b01564(2018) |
| American Chemical Society (ACS) | Role of Ligand Straining in Complexation of Eu3+–Am3+ Ions by TPEN and PPDEN, Scalar Relativistic DFT Exploration in Conjunction with COSMO-RS. Sk. Musharaf Ali. ACS Omega 2018, 3, 13104-13116, DOI: 10.1021/acsomega.8b00933 (2018) |
| 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) |
| 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 | Triazolopyrimidine and triazolopyridine scaffolds as TDP2 inhibitors. Carlos J.A.Ribeiro, Jayakanth Kankanala, Jiashu Xie, Jessica Williams, Hideki Aihara, Zhengqiang Wang. Bioorganic & Medicinal Chemistry Letters 29 (2019) 257–261, DOI: 10.1016/j.bmcl.2018.11.044 (2019) |
| ELSEVIER | SGC based prediction of the flash point temperature of pure compounds. Tareq A. Albahri, Norah A.M. Esmael. Journal of Loss Prevention in the Process Industries 54, July 2018, Pages 303-311, DOI: 10.1016/j.jlp.2018.05.005 (2018) |
| ELSEVIER | Shape selectivity vapor-phase conversion of lignin-derived 4-ethylphenol to phenol and ethylene over acidic aluminosilicates: Impact of acid properties and pore constraint. Yuhe Liao, Martin d’Halluin, Ekaterina Makshina, Danny Verboekend, Bert F.Sels. Applied Catalysis B: Environmental. 234, 15 October 2018, Pages 117-129, DOI: 10.1016/j.apcatb.2018.04.001 (2018) |
| ELSEVIER | Spontaneous motion of various oil droplets in aqueous solution of trimethyl alkyl ammonium with diffrent 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) |
| Oxford Academic | Plant Cuttings. Nigel Chaffey. Annals of Botany, Volume 121, Issue 6, 11 May 2018, Pages iv–vii, DOI: 10.1093/aob/mcy070 (2018) |
| Royal Society of Chemistry (RSC) | Physical Chemistry of Energy Conversion in Self-propelled Droplets Induced by Dewetting Effect. B. NANZAI, T. BAN. In: Self-organized Motion: Physicochemical Design based on Nonlinear Dynamics, 2018 (2018) |
| 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) |
| Springer | Multi-agent System for Forecasting Based on Modified Algorithms of Swarm Intelligence and Immune Network Modeling. Samigulina G.A., Massimkanova Z.A. In: Agents and Multi-Agent Systems: Technologies and Applications 2018. Jezic G., Chen-Burger YH., Howlett R., Jain L., Vlacic L., Šperka R. (eds) KES-AMSTA-18 2018. Smart Innovation, Systems and Technologies, vol 96. Springer, Cham (2018) |
| 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) |
| 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) |
| 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) |
| NCBI | Diversity and Applications of Endophytic Actinobacteria of Plants in Special and Other Ecological Niches. Singh R and Dubey AK. Front. Microbiol. 9:1767. doi: 10.3389/fmicb.2018.01767 (2018) |
| IUCr | The solid-state conformation of the topical antifungal agent O-naphthalen-2-yl N-methyl-N(3-methylphenyl)carbamothioate. Douglas M. Ho and Michael J. Zdilla. Acta Cryst. (2018). C74, 1495–1501 DOI: 10.1107/S2053229618013591(2018) |
| Qazaq university | Construction of an optimal immune network model based on the modified swarm algorithm. G. A. Samigulina, Zh. A. Massimkanova. KazNU Bulletin. Mathematics, Mechanics, Computer Science Series, N.2(98), Aug 2018, Pages 77-87, DOI: 10.26577/jmmcs-2018-2-402 (2018) |
| TEDE | Uma perspectiva da modelagem QSPR para triagem/desenho de catalisadores para a síntese de carbonatos oleoquímicos. Santos, Victor Hugo Jacks Mendes dos. PUCRS(Pontníficia Universidade Católica do Rio Grande do Sul), Available Online at: http://tede2.pucrs.br/tede2/handle/tede/8260 (2018) |
| TAUJA | DETERMINACIÓN DE ESBO EN SIMULANTES. Moreno-Infantes, Rosa L.. UJA(Universidad de Jaén), Available Online at: https://hdl.handle.net/10953.1/8417 (2018) |
| NKU | Aspartamın yapay reseptörlere dayalı moleküler baskılı polimerleri ve moleküler modellenmesi. Sevindik, Yunus. Namık Kemal University Institutional Repository, Available Online at: http://hdl.handle.net/20.500.11776/2622 (2018) |
| 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) |
| ΣΥΝΔΕΣΜΟΣ ΕΛΛΗΝΙΚΩΝ ΑΚΑΔΗΜΑΪΚΩΝ ΒΙΒΛΙΟΘΗΚΩΝ | Εργαστηριακές ασκήσεις 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 |
| 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. |
| 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) |
| US | Electroless copper plating compositions. Meng Qi,Sze Wei Chum,Ping Ling Li. United States Patent 10060034 (2018) |
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 |
Mol-Instincts RESOURCES
Mol-Instincts Cataloue
ChemEssen Catalogue
CEO Profile
Mol-Instincts User Manual
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