예측 정확도
예측 정확도는 평균 95% 이상입니다.
기존의 예측 기술들은 시행착오적 방법에 기반을 둔 경험적 기술들이 대부분이어서, 예측 정확도가 대부분 70% 이하로 낮을 뿐 아니라 적용 가능한 물질의 종류도 제한적인 경우가 많습니다.
저희 켐에쎈의 기술은 양자역학에 기반을 둔 근본적으로 다른 기술입니다.
현존하는 실험 데이터를 거의 모두 수집하여 5년 넘도록 철저한 비교 검증을 거듭한 결과, 평균 예측 정확도가 95% 이상임이 확인되었습니다.
기존 기술의 예측 정확도(Joback방법) 63.07%
(주)켐에쎈사 기술의 예측 정확도 95.02%
2,171개 화학 물질의 끓는점을 예측하여 실험값과 비교시, 널리 알려진 기존의 joback방법은 63.07%의 정확도를 보이는 반면 (주)켐에쎈사의 기술은 95.02%의 예측 정확도를 보여주고 있다.
개발 프로세스
관련 특허 40건 취득
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01
High Quality
Quantum CalculationConformer structure를 분석하여 가장 안정
한 구조를 Quantum 계산의 초기 구조로 사용 -
02
Most Advanced
QSPR Modeling양자역학 결과를 포함한 2,000가지 이상의 molecular descriptor를 기반으로 최적의 QSPR모델 구축
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03
Detailed Model
Verification현존하는 대부분의 실험값(7년이상 수집)과 예측값을 비교하여 정확도가 95% 이상임을 검증
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04
Chemical Property
Categorization다양한 종류의 화합물과 화합물 당 2,100가지 정보를 수록하는 DB 개발 완료
인용 문헌
Mol-Instincts는 네이처 등 권위있는 학술지에 다수 인용되고 있습니다.
| 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) |
특허 리스트
| 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 Chemical 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 |