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Analytical Chemistry

• Characterizing Sulfur Species in Vacuum Residues with Mass Spectrometry, Published in the FUEL

  Led by key researcher Abdul Gani Abdul Jameel, the research team from King Fahd University of Petroleum & Minerals conducted a molecular characterization of sulfur species in vacuum residues using APPI and ESI FT-ICR mass spectrometry. The study utilized data from ChemRTP, which is part of CC-DPS, specifically focusing on the molecular weight, carbon number, DBE, and H/C ratio to calculate the average molecular parameters (AMP) of the vacuum residues. This data was crucial in designing an average surrogate molecule that reflects the overall chemical composition of the sample, aiding in the prediction of various physical and thermo-chemical properties of the vacuum residues. The findings underscore the importance of accurate molecular characterization in fuel analysis. Full Article

• Characterization of Deasphalted Heavy Fuel Oil with Advanced Spectroscopic Techniques, Published in the FUEL

  The research team from King Fahd University of Petroleum & Minerals utilized QSPR-based predictive property data from the ChemRTP, a component of CC-DPS, to evaluate the thermochemical properties of surrogate molecules for deasphalted heavy fuel oil (DAO). This data significantly contributed to assessing fuel properties such as critical pressure, critical temperature, and enthalpy of formation, enhancing the understanding of fuel behavior post-deasphalting. These findings aid in designing better combustion systems and optimizing desulfurization processes. Full Article

• Enhancing MALDI-MS Analysis with Electron-Transfer Secondary-Reaction Matrices, Published in the ACS OMEGA

  The research team led by Cosima D. Calvano from Università degli Studi di Bari Aldo Moro utilized data from the Mol-Instincts, the CC-DPS database system, as supplementary information for their study on matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) of indolenine-based croconaines. Thermodynamic properties, specifically proton affinity and ionization potential, were used to evaluate the performance of different matrices. These properties facilitated the selection of 1,5-diaminonapthalene (DAN) as an optimal matrix, significantly improving the ionization efficiency and structural characterization of croconaine dyes. Full Article

• Quantification of ESBO in Olive Oil using HPLC-MS/MS-TQ, Published in the CREA

  Led by Antonio J. Ortiz Hernández, the research team from Escuela Politécnica Superior de Linares conducted a study on the quantification of Epoxidized Soybean Oil (ESBO) in olive oil using HPLC-MS/MS-TQ. They employed Mol-Instincts data, a database component of CC-DPS, for the chemical formula and properties of ESBO to assist in identifying and calibrating the chromatographic peaks. This study enabled accurate quantification of ESBO in various samples, playing a crucial role in evaluating the migration of ESBO from plastics to food. Full Article

• Advanced MALDI MS Analysis of Photosynthetic Pigments, Published in the JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY

  Led by Cosima Damiana Calvano, the research team from the Università degli Studi di Bari Aldo Moro focused on the MALDI MS analysis of bacteriochlorophylls from Rhodobacter sphaeroides and its zinc and copper analogues. They utilized ionization energy and electron affinity data from the Mol-Instincts, the CC-DPS database system, to evaluate the effectiveness of electron transfer matrices, including 1,5-diaminonaphthalene (DAN). This data contributed to understanding the electron transfer mechanism, aiding in the structural analysis of the pigments. These research findings were used as valuable resources in photosynthetic pigment studies. Full Article

Astrobiology

• Analyzing Chirality as a Biosignature in Extraterrestrial Environments, Published in the NEW ASTRONOMY REVIEWS

  David Avnir from the Institute of Chemistry at the Hebrew University of Jerusalem critically reviewed the potential of chirality as a biomarker for extraterrestrial life. The study utilized specific molecular data from the Mol-Instincts, the CC-DPS database system, to assess chirality indicators' reliability in various celestial environments. This data supported the analysis of potential chiral molecules in exoplanetary atmospheres, contributing significantly to the study's insights into astrobiological exploration. Full Article

Astrophysics and Radiation Biology

• Analyzing the Impact of High-Energy Cosmic Rays on Astronaut Eye Tissue, Published in the OAKTRUST

  In this study authored by Bridger Freeman, a comprehensive analysis was conducted on the interactions of high-energy cosmic rays with eye tissue. The research utilized Type II collagen fragment structural data from the Mol-Instincts, the CC-DPS database system, employing the TRIM (Transport of Ions in Matter) software to analyze the damage caused by cosmic rays at various energy levels (20 MeV, 1 GeV, and 10.08 GeV). The study focused on understanding the relationship between nuclear mass and the extent of tissue damage and evaluated the effectiveness of different shielding materials in protecting eye tissue. The Mol-Instincts data provided critical insights into this relationship, significantly contributing to the findings. Full Article

Battery Chemistry

• Investigating the Flammability Limits of Electrolyte Solvents in Lithium-Ion Batteries, Published in the EXPERIMENTAL THERMAL AND FLUID SCIENCE

  The research team led by Feng Guo from Hokkaido University explored the flammability limits of electrolyte solvents using the wick combustion method. The study examined how different OPC (organophosphorus compound) additives impacted the limiting oxygen concentration (LOC) of these solvents. ChemRTP's heat of combustion values, a component of CC-DPS, were used to correlate wick-LOC with other flammability properties, enhancing the accuracy of their flammability assessments. This data was crucial for validating the effectiveness of flame-retardant additives in lithium-ion battery electrolytes. Full Article

Biochemistry

• In-silico Analysis of Black Pepper Extract’s Efficacy Against Pulm Pox Virus, Published in the INDIAN JOURNAL OF NATURAL SCIENCES

  Pratibha Kumari Behera and Preetha Bhadra from Centurion University of Technology and Management conducted a study analyzing the effects of Black Pepper extract on the Pulm Pox Virus. Utilizing SDF files from the Mol-Instincts, the CC-DPS database system, the researchers gathered 3D structures of black pepper’s pharmacophores to perform molecular docking studies. This data significantly contributed to identifying the binding affinity and interaction strength of black pepper compounds with viral proteins, offering promising insights into the development of natural biopesticides. Full Article

Bioinformatics

• Investigating Pirfenidone's Mechanism of Action Post-Myocardial Infarction, Published in the SCIENTIFIC REPORTS

  Led by key researcher Alberto Aimo, the research team from Sant'Anna School of Advanced Studies utilized the SDF file for pirfenidone from the Mol-Instincts, the CC-DPS database system, to model its binding affinity towards predicted direct targets such as Furin and MAPK12. These simulations revealed high binding affinities, indicating significant biological interactions. The study employed these data to analyze pirfenidone’s potential therapeutic effects on post-myocardial infarction (MI) remodeling, showing its contribution to understanding drug efficacy and mechanisms of action. Full Article

Biophysical Chemistry

• Investigating the Binding Mechanism of Crocin to β-Lactoglobulin, Published in the FOOD HYDROCOLLOIDS

  The research team led by Zahra Allahdad from the University of Tehran conducted a study on the binding interactions between crocin and β-Lactoglobulin (β-LG). Utilizing molecular docking and molecular dynamics simulations, the team employed structural data on crocin from the Mol-Instincts, the CC-DPS database system, to determine the number of rotatable bonds. This data was crucial in accurately simulating the binding mechanism and predicting the stability of the crocin-β-LG complex. The insights gained from this study contribute to understanding the thermodynamic properties and stability of such complexes in food systems. Full Article

Biotechnology

• In-silico Docking Analysis of Ajwain Extract Compounds Against Aster Yellow Genes, Published in the INDIAN JOURNAL OF NATURAL SCIENCES

  Led by Preetha Bhadra, the research team from Centurion University of Technology and Management conducted an in-silico study to analyze the effects of Ajwain extract on genes responsible for Aster Yellow disease. The team utilized SDF files from Mol-Instincts, the CC-DPS database system, to prepare and optimize the structures of pharmacophores like Thymol, Lupeol, and Gamma Terpinene. This data was crucial for the docking studies performed in Discovery Studio, allowing for the identification of strong binding affinities and potential therapeutic compounds. The findings highlight the potential of Ajwain extracts in agricultural biotechnology. Full Article

• Analysis of Cardamom Extracts for Aster Yellow Disease Treatment, Published in the INDIAN JOURNAL OF NATURAL SCIENCES

  The research team led by Preetha Bhadra from Centurion University of Technology and Management conducted a study on the effects of cardamom extract on plant disease using in-silico methods. The team utilized various pharmacophores of cardamom, including SDF files from the Mol-Instincts, the CC-DPS database system. These data were instrumental in docking studies to identify potential therapeutic compounds. The study results indicated that specific bioactive components of cardamom might have a positive effect on treating Aster Yellow disease. Full Article

Botany

• Root-Fungal Associations and Their Geological Impact, Published in the ANNALS OF BOTANY

  Nigel Chaffey from Bath Spa University reviewed the impact of root-fungal associations on alluvial formations using stratigraphic data. This study referenced structural data from Mol-Instincts, the CC-DPS database system. This data contributed to identifying the evolutionary significance of root systems in terrestrial ecosystems, linking vegetation cover to enhanced sediment retention on land. The findings emphasize the transformative power of plant roots in shaping geological landscapes. Full Article

Catalysis and Reaction Engineering

• Thermodynamic Analysis and Reaction Pathway Optimization in Zeolite-Catalyzed Dealkylation, Published in the APPLIED CATALYSIS B: ENVIRONMENT AND ENERGY

  Led by key investigator Y. Liao, the research team from KU Leuven utilized data from the Mol-Instincts, the CC-DPS database system, to calculate Gibbs Free Energy and optimize the catalytic dealkylation of 4-ethylphenol into phenol and ethylene over acidic aluminosilicates. This data enabled precise thermodynamic modeling of the reaction pathways, allowing for the identification of optimal catalysts and reaction conditions. The researchers evaluated the impact of various catalysts' pore structures on catalytic performance and stability based on these calculations. Full Article

Catalysis Chemistry

• Thermodynamic Analysis of 4-n-PP Dealkylation to Phenol, Published in the ACS CATALYSIS

  Led by investigator Bert F. Sels, the research team from KU Leuven utilized thermodynamic data from the Mol-Instincts, the CC-DPS database system, to analyze the dealkylation of 4-n-propylphenol (4-n-PP) over acidic zeolites. Mol-Instincts data assisted in modeling reaction pathways and predicting product distributions. This data contributed to demonstrating the reaction's thermodynamic favorability and optimizing reaction conditions for phenol production. Full Article

Chemical Engineering

• Multicomponent Evaporation of Diesel Fuel Droplets, Published in the FUEL

  Led by Jörn Hinrichs, the research team from RWTH Aachen University conducted an experimental and computational study on the multicomponent evaporation of diesel fuel droplets. The study utilized predictive data from ChemRTP, a component of CC-DPS, to accurately simulate the evaporation behavior of different fuel compositions. This data was pivotal in developing a robust Discrete Continuous Multicomponent (DCMC) model, which was validated through experimental observations using an acoustic levitator. The research results showed that the new fuel composition model reproduced the experimental data more accurately than the composition models from existing literature. Full Article

• Application of Physico-Chemical Properties in Biogas Desulfurization, Published in the CHEMICAL ENGINEERING RESEARCH AND DESIGN

  The research team led by Rodrigo Rivera-Tinoco from MINES ParisTech utilized the ChemRTP, a component of CC-DPS, to estimate the physico-chemical properties of (CH3)2Si(HSO4)2. These properties were critical for modeling the separation methods of H2S from methane in biogas streams. By leveraging ChemRTP data, the researchers were able to accurately simulate the process conditions in Aspen Plus, optimizing the design of a novel biogas purification unit. This study underscores the importance of reliable chemical property data in enhancing process efficiency and environmental compliance in biogas purification technologies. Full Article

Chemical Physics

• Investigating Electron Scattering Cross Sections in Symmetric Ether Molecules, Published in the INTERNATIONAL JOURNAL OF MASS SPECTROMETRY

  The research team led by Paresh Modak from the Department of Applied Physics at Indian Institute of Technology referenced ionization potential and polarizability data from the Mol-Instincts, the CC-DPS database system, to analyze the electron scattering cross sections of various symmetric ether molecules. This data was crucial for accurately calculating the electron scattering cross sections, which is essential for understanding the electron impact ionization processes. The team used this data to elucidate the relationship between molecular structure and electron scattering properties, providing significant insights into the electron collision characteristics of symmetric ether molecules. Full Article

• Application of Physico-Chemical Properties in Biogas Desulfurization, Published in the CHEMICAL ENGINEERING RESEARCH AND DESIGN

  The research team led by Rodrigo Rivera-Tinoco from MINES ParisTech utilized the ChemRTP, a component of CC-DPS, to estimate the physico-chemical properties of (CH3)2Si(HSO4)2. These properties were critical for modeling the separation methods of H2S from methane in biogas streams. By leveraging ChemRTP data, the researchers were able to accurately simulate the process conditions in Aspen Plus, optimizing the design of a novel biogas purification unit. This study underscores the importance of reliable chemical property data in enhancing process efficiency and environmental compliance in biogas purification technologies. Full Article

Chemical Research on Olfactory Influence

• Exploring the Impact of Odorants on Heart Rate Variability, Published in the SCIENTIFIC REPORTS

  Led by investigator Hamidreza Namazi, the research team from Nanyang Technological University utilized Entropy data from the Mol-Instincts, the CC-DPS database system, to explore the relationship between olfactory stimuli and heart rate variability. Their study demonstrated that odorants with higher entropy levels resulted in significant changes in the fractal dynamics and approximate entropy of heart rate signals, as measured by ECG. This research provides insights into how different odorants can influence heart activity, potentially aiding in the development of therapeutic strategies for heart disease rehabilitation. Full Article

Combustion Chemistry

• Analysis of Ignition Delay Times for Decene and Decane Isomers, Published in the ENERGY & FUELS

  Researchers Aniket Tekawade, Tianbo Xie, and Matthew A. Oehlschlaeger from Rensselaer Polytechnic Institute conducted a study on the ignition delay times of 1-decene, trans-5-decene, and n-decane using both constant-volume spray and shock-tube experiments. The study utilized enthalpy of vaporization data from the Mol-Instincts, the CC-DPS database system. This data helped accurately compare the reactivity trends of the compounds under various conditions and was useful for validating and developing kinetic models for high-molecular-weight alkenes. Full Article

• Investigating Diacetyl Flame Kinetics for Ketene Mechanism Development, Published in the COMBUSTION AND FLAME

  Led by investigator Wenyu Sun, the research team from Tsinghua University conducted a detailed kinetic investigation of diacetyl flames to develop effective constraints for ketene combustion mechanisms. The study utilized critical pressure and temperature data from the ChemRTP, a component of CC-DPS, which provided accurate predictions for the properties of various adducts essential in their kinetic models. These data were instrumental in refining reaction pathways and validating experimental results, significantly contributing to the accuracy of their kinetic model. Full Article

• Impact of Organophosphorus Compounds on Electrolyte Flammability, Published in the COMBUSTION AND FLAME

  Led by investigator Osamu Fujita, the research team from Hokkaido University conducted a study on the flame stability limits of lithium-ion battery (LIB) electrolyte solvents with the addition of organophosphorus compounds (OPCs). The study referenced ChemRTP, a component of CC-DPS, for obtaining physical properties data such as boiling points and heats of combustion of the chemicals involved. This data was essential in analyzing the flammability characteristics of various electrolyte mixtures, thereby providing critical insights into enhancing the fire safety of LIBs. These findings contribute significantly to the development of safer battery technologies. Full Article

Computational Chemistry

• Evaluating Cannabinoids as Inhibitors of SARS-CoV-2 PLpro, Published in the VIRUSES

  The research team led by Shahidul M. Islam from Delaware State University explored the potential of cannabinoid compounds as inhibitors of SARS-CoV-2 papain-like protease (PLpro) through computational analysis. They used SDF files from the Mol-Instincts, the CC-DPS database system, which were employed in molecular docking simulations to evaluate the binding affinities of cannabinoids to PLpro. The study found that cannabinoid compounds formed strong bonds with PLpro, suggesting their potential to inhibit the replication of SARS-CoV-2. Full Article

• Investigating the properties of doped BETS molecules, Published in the JOURNAL OF MOLECULAR MODELING

  Led by investigator Zounedou Ntieche, the research team from the University of Yaounde I utilized structural data files from the Mol-Instincts, the CC-DPS database system, to initiate the ab-initio calculations for the undoped and doped bis (ethlenedithio) tetraselenafulvalene (BETS) molecules. This data served as a starting point for geometry optimization and helped in studying the electronic, nonlinear optical, and thermodynamic properties of the compounds. The accurate geometric parameters provided crucial insights into understanding the material properties. These findings were published in the journal Electronic, Non-Linear Optical, Optoelectronic and Thermodynamic properties. Full Article

• In Silico Analysis of the Multi-Targeted Mode of Action of Ivermectin, Published in the COMPUTATION

  Maral Aminpour and colleagues from University of Alberta conducted an in silico analysis of ivermectin and related compounds targeting SARS-CoV-2. The research team utilized the Molecular Operating Environment (MOE) software for ligand preparation and docking studies, employing various computational techniques to evaluate ligand-protein interactions. They used 3D structural data from the Mol-Instincts, the CC-DPS database system, to visualize and analyze the structure of ivermectin. The study revealed that ivermectin and similar compounds have high binding affinities for the spike protein, CD147 receptor, and α7 nicotinic acetylcholine receptor. These findings provide valuable insights into the mechanisms by which ivermectin may limit the infectivity and morbidity of SARS-CoV-2. Full Article

• Investigating the Efficacy of Stevia Extract as a Biopesticide, Published in the INDIAN JOURNAL OF NATURAL SCIENCES

  The research team of D. Gayatri and Preetha Bhadra from Centurion University of Technology and Management studied the efficacy of Stevia extract as a biopesticide for leaf blight. They utilized SDF files from the Mol-Instincts, the CC-DPS database system. In this study, the 3D structures of Stevia pharmacophores were used for molecular docking analysis. The SDF files from Mol-Instincts provided basic data to understand the structural characteristics of Stevia compounds and to perform molecular docking experiments. This data helped in evaluating the potential of Stevia as a biopesticide. Full Article

• Investigating the Anti-Diabetic Properties of Stevia Extracts, Published in the INDIAN JOURNAL OF NATURAL SCIENCES

  Researchers D. Gayatri and Preetha Bhadra from Centurion University of Technology and Management conducted a study to evaluate the effects of Stevia rebaudiana extract on diabetes. The team obtained various pharmacophores and retrieved their corresponding structures' SDF files from the Mol-Instincts, the CC-DPS database system, for molecular docking studies. These data facilitated the identification of the binding affinities of Stevia compounds with target proteins associated with diabetes. The study demonstrated that these compounds, particularly N-methyltyramine and dalbergioidin, showed significant interaction scores and ADMET properties, supporting their potential as natural anti-diabetic agents. Full Article

• In Silico Study of Cocoa Phytochemicals against Streptococcus Pneumoniae Enzyme, Published in the EUROPEAN JOURNAL OF MEDICINAL PLANTS

  Led by investigator Sunanya Das, the research team from Centurion University of Technology and Management, Odisha, India, utilized SDF files from Mol-Instincts, the CC-DPS database system, to conduct a molecular docking study. They investigated the interaction between cocoa phytochemicals and ribitol-5-phosphate 2-dehydrogenase, an enzyme in Streptococcus pneumoniae. The study identified luteolin and chlorogenic acid as potent inhibitors, playing a crucial role in disrupting the pathogen's metabolic pathways. These findings highlight the potential of cocoa phytochemicals as therapeutic agents against pneumonia. Full Article

• Identifying Phytochemicals in Coriandrum Sativum for Inhibiting Tooth Cavity Bacteria, Published in the PLANT CELL BIOTECHNOLOGY AND MOLECULAR BIOLOGY

  Led by investigator Mukundjee Pandey, the research team from Centurion University of Technology and Management conducted a study to identify phytochemicals in Coriandrum sativum that could inhibit the cyclopropane-fatty-acyl-phospholipid synthase enzyme in Lactobacillus casei, a key contributor to tooth cavities. Utilizing molecular information (SDF files) from the Mol-Instincts, the CC-DPS database system, the team determined that petroselinic acid effectively deactivates this enzyme. The study highlights the potential of this phytochemical in disrupting the biological cycle of Lactobacillus, providing a theoretical basis for the medicinal use of Coriandrum sativum in treating dental caries. Full Article

• Optimization and Spectral Analysis of Energetic Materials Using TDDFT, Published in the INTERNATIONAL FORUM ON FRONTIERS IN ENERGETIC MATERIALS

  Douglas V. Nance, an independent research scientist, conducted a study on Nitromethane (NM) and Cyclotrimethylenetrinitramine (RDX) using the OCTOPUS TDDFT program. The initial atomic positions for NM were obtained from the Mol-Instincts, the CC-DPS database system, in the form of a SDF file, which facilitated geometry optimization and subsequent spectral analysis. This data provided accurate molecular structures, enhancing the reliability of the study and contributing to insights into the interaction of electromagnetic radiation with energetic materials. Full Article

• Development of Multi-Agent Technology for Predicting Drug Structure-Property Dependence, Published in the INTERNATIONAL WORK-CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING

  The research team led by Galina Samigulina and Zarina Samigulina from the Almaty Institute of Information and Computing Technologies in Kazakhstan developed a technology for predicting the structure-property dependence of drugs using modified algorithms of artificial immune systems. The study utilized the Mol-Instincts, the CC-DPS database system, as one of the descriptor databases to optimize drug molecular structures. These data were essential for accurate prediction and comparison of drug properties, significantly enhancing the reliability of the research. Full Article

• Optimization of Immune Network Models for Drug Design, Published in the JOURNAL OF MATHEMATICS, MECHANICS AND COMPUTER SCIENCE

  Led by investigators Galina A. Samigulina and Zh. A. Massimkanova, the research team from Kazakh-British Technical University used descriptor data from the Mol-Instincts, the CC-DPS database system, to develop an optimal immune network model for predicting QSAR of sulfanilamide drug compounds. Utilizing over 1,000 descriptors from Mol-Instincts, they compared classical PSO and modified IWPSO algorithms to select the most informative descriptors. This data was crucial in enhancing the accuracy and efficiency of their predictive models for new drug candidates. Full Article

• Predicting the Magnetic Susceptibility of Membrane Proteins, Published in the JOURNAL OF CHEMICAL THEORY AND COMPUTATION

  Led by investigator Mahnoush Babaei, the research team from Carnegie Mellon University utilized the Mol-Instincts, the CC-DPS database system, to obtain average molar magnetic susceptibility data for six amino acids to calculate the magnetic properties of membrane proteins. The team proposed a novel mixed-methods approach that uses structural subunits of complex molecules to predict the magnetic susceptibility and anisotropy of membrane proteins. This data was crucial for efficiently calculating the magnetic properties of complex molecules, revealing the influence of specific amino acid residues and protein structures on magnetic characteristics. Full Article

Corrosion Chemistry

• Investigating the Impact of Carbozoline and Etidronic Acid on Corrosion Inhibition, Published in the FIRE SAFETY

  The research team led by T. M. Voitovych from Lviv State University of Life Safety conducted a study on the influence of carbozoline and etidronic acid on the corrosion activity of foam concentrate solutions. They utilized structural and deep data of etidronic acid from the Mol-Instincts, the CC-DPS database system, to understand its chemical behavior and effectiveness as a corrosion inhibitor. This data was crucial in analyzing and comparing the corrosion rates in different foam solutions, demonstrating that etidronic acid significantly reduces corrosion rates, enhancing the longevity and effectiveness of firefighting foam concentrates. Full Article

Crystallography and Structural Chemistry

• Analyzing Tolnaftate Conformations through X-ray Crystallography, Published in the ACTA CRYSTALLOGRAPHICA SECTION C: STRUCTURAL CHEMISTRY

  Led by investigator D.M. Ho, the research team from Temple University utilized theoretical structural data from the Mol-Instincts, the CC-DPS database system, to compare with experimental X-ray crystallographic data of the antifungal agent tolnaftate. They found significant discrepancies between the theoretical models and the actual solid-state conformations. The study highlighted the limitations of current theoretical models, emphasizing the need for critical reassessment of such models in predicting molecular geometries and interactions. This research demonstrates the importance of experimental validation in structural chemistry. Full Article

Data Mining in Chemistry

• Predicting Structure-Property Relationships of Sulfonamides, Published in the JOURNAL OF THE OPEN SYSTEMS EVOLUTION PROBLEMS

  Researchers G.A. Samigulina and Z.I. Samigulina from the Institute of Information and Computing Technologies, Kazakhstan, conducted a study on the computer-aided molecular design of new sulfanilamide drugs. They utilized the Mol-Instincts, the CC-DPS database system, to develop a comprehensive set of sulfonamide descriptors. These descriptors were then processed using the Rapid Miner software, applying Random Forest and Principal Component Analysis algorithms. This data was crucial for numerical modeling and the visualization of the data in 3D, significantly contributing to the accurate prediction of structure-property relationships in sulfonamides. Full Article

Electrochemistry

• Enhancing Cryogenic Performance of Lithium-Ion Batteries, Published in the ENERGY & ENVIRONMENTAL SCIENCE

  In a study led by Yoon-Gyo Cho, the research team from UNIST utilized enthalpy data from the Mol-Instincts, the CC-DPS database system, to confirm the eutectic temperature of their electrolyte compositions. This data was crucial for accurately estimating the eutectic point and phase boundaries in their ternary mixture of EC, DMC, and nitriles. By leveraging this information, the team was able to design electrolytes that significantly improved the performance of lithium-ion batteries at cryogenic temperatures, thus enabling faster charge and discharge cycles. Full Article

Environmental Chemistry

• Advancing Environmental Chemical Analysis with High-Throughput Data, Published in the JOURNAL OF CHEMINFORMATICS

  Led by investigator Richard S. Judson, the research team from the U.S. Environmental Protection Agency utilized data from ChemRTP, a component of CC-DPS, in their study on environmental chemicals' toxicity. The ChemRTP provided essential physicochemical properties and bioactivity data necessary for the research. These data were used for screening and prioritizing chemicals, contributing to the development of computational toxicology models in the study. Full Article

• Estimating Microbial Growth Yields for Biodegradation of Pesticides, Published in the SAR AND QSAR IN ENVIRONMENTAL RESEARCH

  Led by investigator A. L. Brock, the research team from the Technical University of Denmark utilized Gibbs Free energy data from the Mol-Instincts, the CC-DPS database system, to predict the microbial growth yields during the degradation of pesticides. This data was used to improve the accuracy of comparisons with experimentally determined growth yields. The team assessed the potential formation of biogenic non-extractable residues (NER) during pesticide degradation. This research significantly contributed to understanding NER data in pesticide degradation experiments. Full Article

Environmental Toxicology

• Linking Chemicals to Adverse Outcome Pathways Using Diverse Databases, Published in the ALTERNATIVES TO ANIMAL EXPERIMENTATION

  The research team led by Natàlia Garcia-Reyero from the U.S. Army Engineer Research and Development Center conducted a study on methodologies and tools for linking chemicals to adverse outcome pathways (AOPs). This study referenced various databases, including Mol-Instincts, the CC-DPS database system, to assess their utility in predicting molecular interactions and adverse outcomes. The results demonstrated that the proposed methodology and the use of these databases effectively predict adverse outcome pathways and can serve as valuable tools in regulatory decision-making. Full Article

Fire Safety Engineering

• Evaluation of Fire Extinguishing Agent Performance in Electrical Fires, Published in the JOURNAL OF THE KOREA ACADEMIA-INDUSTRIAL COOPERATION SOCIETY

  Professor Young-Sam Lee from Osan University and CEO Soo-Ho Baek from Space Power Company conducted a study to develop a microcapsule-based fire extinguisher for electric distribution boards. By utilizing data on the molecular structure and properties of perfluoro(2-methyl-3-pentanone) from the Mol-Instincts, the CC-DPS database system, they were able to design an effective extinguishing agent. This data played a crucial role in optimizing the fire extinguishing properties, allowing for the rapid suppression of n-heptane fires within an average of 4.48 seconds. Full Article

Food Chemistry

• Analysis of Casein's Role in Various Applications, Published in the SCIENCE WORLD

  The research team led by Sana Datta Vasuki Satyanarayana from Sam Higginbottom University of Agriculture, Technology and Sciences conducted an extensive study on the various applications of casein, referencing its structure from the Mol-Instincts, the CC-DPS database system. This study explored the physicochemical properties of casein and its diverse applications in food and non-food industries. The data contributed to the team's analysis of casein's physical properties and potential industrial uses. Full Article

Food Science and Nutrition

• Utilization of Databases for Anticancer Activity Analysis of Food Components, Published in the MOLECULES

  Piotr Minkiewicz and colleagues from the University of Warmia and Mazury conducted a comprehensive review using multiple databases, including ChemRTP, a component of CC-DPS, to analyze the anticancer properties of food components. The research team played a crucial role in predicting the physico-chemical properties of compounds and identifying bioactive substances. This data significantly contributed to evaluating the potential health benefits of various dietary components and underscored the utility of databases in food science and nutrition research. These findings enhanced the understanding of the anticancer activity of food components. Full Article

Fuel Chemistry

• Structural Characterization of Heavy Fuel Oils Using NMR Spectroscopy, Published in the ENERGY & FUELS

  A research team led by S.M. Sarathy from King Fahd University of Petroleum & Minerals (KAUST) utilized entropy and enthalpy data derived from the Mol-Instincts, the CC-DPS database system. These data played a crucial role in predicting physical properties such as critical pressure, critical temperature, and entropy of surrogate molecules representing heavy fuel oils (HFOs). These predictions were validated against experimental measurements, significantly contributing to understanding the molecular structure and combustion characteristics of HFOs. Full Article

Materials Chemistry

• Analysis of Dielectric Properties in Chitosan-Metal Oxide Nanocomposites, Published in the POLYMERS

  Led by investigator Azza Abou Elfadl, the research team from Fayoum University and Taibah University utilized 3D structure data from Mol-Instincts, the CC-DPS database system, to study the chemical hardness and electronic properties of chitosan-metal oxide nanocomposites. The team optimized the structures of CoO and SrO nanoparticles stabilized within the chitosan matrix and analyzed their electronic properties. This research evaluated the dielectric properties of the chitosan-metal oxide nanocomposites, demonstrating their potential for various electronic material applications. Full Article

Medicinal Chemistry

• Evaluation of TDP2 Inhibitors for Cancer Treatment, Published in the BIOORGANIC & MEDICINAL CHEMISTRY LETTERS

  Researchers led by C.J.A. Ribeiro from the University of Minnesota conducted a study to validate and optimize tyrosyl-DNA phosphodiesterase 2 (TDP2) inhibitors. They utilized chemical structure data from Mol-Instincts, the CC-DPS database system, for compound P10A10 (Chembridge ID 7236827) to confirm its structure as the 5-phenyl triazolopyrimidine regioisomer 7a. This data played a crucial role in their structure-activity relationship studies, leading to the synthesis of 47 analogues, four of which exhibited significant TDP2 inhibition. These findings underscore the potential of these compounds as chemical probes for studying TDP2's role in DNA repair. Full Article

• Identifying Phytochemicals Effective Against Tuberculosis, Published in the EUROPEAN JOURNAL OF MEDICINAL PLANTS

  Led by investigator Mukundjee Pandey, the research team from Centurion University of Technology and Management utilized SDF files from Mol-Instincts, the CC-DPS database system, for molecular docking data to analyze phytochemicals from Mucuna pruriens. The study specifically focused on L-Dopa and its interaction with the 3-hydroxyacyl-CoA dehydrogenase enzyme, crucial for the survival of Mycobacterium tuberculosis. The data revealed that L-Dopa effectively deactivates this enzyme, thereby interrupting the lipid metabolic cycle of the bacterium. These findings are crucial for developing plant-based treatments for tuberculosis. Full Article

• Evaluating Neem Leaves Phytochemicals for Antifungal Properties, Published in the EUROPEAN JOURNAL OF MEDICINAL PLANTS

  The research team led by Sunanya Das at Centurion University of Technology and Management utilized SDF files from Mol-Instincts, the CC-DPS database system, to conduct molecular docking studies on neem leaf phytochemicals against the enzyme sterol 14-alpha demethylase of Microsporum sp. These data were critical in identifying Glutamic acid and Oleic acid as effective inhibitors, potentially disrupting the pathogen's metabolic pathways. The researchers analyzed how effective neem leaf phytochemicals are in inhibiting pathogens that cause skin diseases. Full Article

• In Silico Analysis of Phytochemicals from Coriandrum Sativum Against Measles, Published in the PLANT CELL BIOTECHNOLOGY AND MOLECULAR BIOLOGY

  The research team led by Swatiprava Panda at Centurion University of Technology and Management conducted a study to identify phytochemicals in Coriandrum sativum that inhibit the Measles virus. They utilized SDF files from the Mol-Instincts, the CC-DPS database system, to model interactions between these compounds and the Alcohol Dehydrogenase enzyme of the virus. Petroselinic acid was identified as the most effective, showcasing significant inhibitory activity. This data provided crucial insights into the potential therapeutic application of coriander phytochemicals against measles. Full Article

Microbial Chemistry

• Structural Analysis of Bioactive Flavonoids in Endophytic Actinobacteria, Published in the FRONTIERS IN MICROBIOLOGY

  Led by investigators Radha Singh and Ashok K. Dubey from the Netaji Subhas Institute of Technology, the research team investigated the diversity and applications of endophytic actinobacteria. Using chemical structure data from the Mol-Instincts, the CC-DPS database system, specifically for 7-Methoxy-3,3′,4′,6-tetrahydroxyflavone, they analyzed bioactive flavonoids. This data helped in identifying and classifying the chemical structures, enabling the team to elucidate the structural characteristics of compounds produced by various endophytic actinobacteria. These findings could provide a significant foundation for the development of new pharmaceuticals in the future. Full Article

Nanomaterials Chemistry

• Optimization of Anticancer Drug Adsorption on Carbon Nanocarriers, Published in the SCIENTIFIC REPORTS

  Investigator Kanes Sumetpipat from Kamnoetvidya Science Academy led a team, including Duangkamon Baowan and Prangsai Tiangtrong, to study the adsorption of chemotherapy drugs on nanocarriers. The study used coordinates of C60 fullerene obtained from the Mol-Instincts, the CC-DPS database system, to model and optimize drug adsorption through the U-NSGA-III algorithm. This data was crucial for determining stable configurations and interaction energies, allowing precise analysis and prediction of drug molecule arrangements. This significantly reduced computational time, provided important insights into drug delivery efficiency, and greatly contributed to the design and development of nanocarriers. Full Article

Nanotechnology

• Interfacial Ligand Exchange and Migration in Gold Nanoparticle Monolayers, Published in the SCIENTIFIC REPORTS

  The research team led by Guang Yang and Daniel T. Hallinan at Florida State University explored the self-assembly of gold nanoparticle (Au NP) monolayers at a three-phase interface. The study utilized parachor values from the Mol-Instincts, the CC-DPS database system, to calculate and optimize the interfacial tension of a hexane and chloroform mixture. This data enabled accurate modeling of nanoparticle interactions, resulting in the successful formation of highly ordered Au NP monolayers. These findings provide significant insights for the field of surface-enhanced Raman scattering (SERS). Full Article

Neuroscience

• Influence of Odorant Complexity and Entropy on EEG Signal Dynamics, Published in the BIOMED RESEARCH INTERNATIONAL

  In a study led by Hamidreza Namazi from Nanyang Technological University, the research team explored the relationship between the complexity and entropy of odorants and the fractal dynamics and entropy of EEG signals. Utilizing entropy data for various odorants obtained from the Mol-Instincts, the CC-DPS database system, the researchers found that more complex and higher entropy odorants resulted in significant changes in the fractal dimension and approximate entropy of EEG signals. This data was crucial for understanding the coupling between odorant characteristics and EEG signal responses, potentially aiding in the analysis of brain activity in response to external stimuli. Full Article

Pharmaceutical Chemistry

• Enhancement of Ciprofloxacin Delivery via Gastroretentive Systems, Published in the POLYMERS

  Investigators Mershen Govender, Thankhoe A. Rants’o, and Yahya E. Choonara from the University of the Witwatersrand developed a novel gastroretentive system to enhance the delivery of narrow absorption window drugs, specifically ciprofloxacin. The research utilized the 3D structure of chitosan (CT1078683894) provided by the Mol-Instincts, the CC-DPS database system, to optimize polymer interactions. The research team evaluated molecular interactions between ciprofloxacin and the polymer carrier system to design an optimal formulation. The study found that this system played a crucial role in controlling drug release and increasing bioavailability. Full Article

• Assessing Smart-Technology for Drug Property Prediction, Published in the HERALD OF THE KAZAKH-BRITISH TECHNICAL UNIVERSITY

  Investigators G.A. Samigulina and Z.I. Samigulina from the Institute of Information and Computational Technologies and the Kazakhstan-British Technical University conducted research on predicting the properties of sulfonamide compounds using Smart-technology. They utilized the Mol-Instincts, the CC-DPS database system, for the initial formation of sulfanilamide descriptors. The Mol-Instincts data helped in building an optimal set of descriptors, which allowed for the effective application of modern optimization algorithms. The use of this data contributed to enhancing the accuracy of predicting structure-property relationships. Full Article

• Ontological Model for Predicting Drug Properties Using Artificial Intelligence, Published in the THEORETICAL BIOLOGY AND MEDICAL MODELLING

  Led by investigators Galina Samigulina and Zarina Samigulina from Kazakh-British Technical University, the research team developed a multi-agent Smart-system for predicting the structure-property dependence of drug compounds. This study utilized the Mol-Instincts, the CC-DPS database system, specifically focusing on sulfonamide descriptors, to enhance the accuracy of QSAR models using modified grey wolf optimization and artificial immune systems algorithms. The Mol-Instincts data was pivotal in creating an optimal set of descriptors, significantly contributing to the efficiency and reliability of the prediction models. Full Article

• Evaluating the Efficacy of Phytochemicals Against Fungal Infections, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Led by investigator Dipankar Bhattacharyay, the research team from Centurion University of Technology and Management utilized SDF files obtained from the Mol-Instincts, the CC-DPS database system, to analyze interactions between phytochemicals in Syzygium aromaticum and the enzyme Laccase from Trichophyton rubrum. The data revealed that Myricetin had the highest potential for deactivating the enzyme, suggesting its effectiveness in treating infections. Full Article

• Molecular Docking Analysis of Phytochemicals Against Escherichia coli, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Led by investigator Mukundjee Pandey, the research team from Centurion University of Technology and Management conducted a study on the efficacy of phytochemicals from guava seeds in inhibiting Escherichia coli, which causes diarrhea. Using the Discovery Studio module of Biovia software, they performed molecular docking studies to identify key interactions. Specifically, SDF files for the phytochemicals were obtained from the Mol-Instincts, the CC-DPS database system. The study identified that heptadecanoic acid and lauric acid effectively deactivate the shikimate dehydrogenase enzyme, crucial for bacterial survival, demonstrating the medicinal potential of guava seeds. Full Article

• Identification of Phytochemicals Inhibiting Escherichia coli Alcohol Dehydrogenase, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  The research team led by Dipankar Bhattacharyay from Centurion University of Technology and Management conducted a study to identify phytochemicals from Bixa orellana L. that inhibit the alcohol dehydrogenase enzyme of Escherichia coli. Using molecular docking techniques via the Discovery Studio module of Biovia software, SDF files for phytochemicals were sourced from Mol-Instincts, the CC-DPS database system. The study found that benzoic acid, acetic acid, phenol, and anthraquinone effectively deactivate the enzyme, highlighting their potential to treat jaundice caused by E. coli. Full Article

• Investigating Guava Seeds for Dysentery Treatment, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Led by investigator Mukundjee Pandey, the research team from Centurion University of Technology and Management in Odisha, India, conducted a study on the efficacy of guava seed phytochemicals against dysentery. Utilizing the Mol-Instincts, the CC-DPS database system, they obtained SDF files for various phytochemicals to perform molecular docking using the Discovery Studio module of Biovia software. The study found that heptadecanoic acid and palmitic acid could significantly inhibit the alcohol dehydrogenase enzyme in Entamoeba histolytica, providing theoretical support for the medicinal value of guava seeds in treating dysentery. Full Article

• Evaluating the Inhibitory Effects of Nigella sativa Phytochemicals on Bordetella Pertussis, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Led by investigator Mukundjee Pandey, the research team from Centurion University of Technology and Management utilized SDF files from the Mol-Instincts, the CC-DPS database system, to identify key phytochemicals in Nigella sativa that inhibit the enzyme adenylate cyclase in Bordetella pertussis. The study found that p-cymene, carvacrol, and thymol were the most effective phytochemicals, significantly contributing to the interruption of the microbial life cycle. These findings enhance our understanding of the medicinal properties of Nigella sativa against cough caused by Bordetella pertussis. Full Article

• Molecular Docking Study of Boswellia Serrata Phytochemicals against Skin Disease, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Led by investigator Gyanranjan Mahalik, the research team from Centurion University of Technology and Management conducted a study on the phytochemicals derived from Boswellia serrata to combat skin disease caused by Staphylococcus aureus. The team used molecular docking methods to identify key interactions between the phytochemicals and the shikimate dehydrogenase enzyme of the bacteria. Utilizing SDF files from the Mol-Instincts, the CC-DPS database system, the analysis revealed that P-cymene and boswellic acid had significant inhibitory effects, providing a theoretical basis for the traditional use of B. serrata in treating skin diseases. Full Article

• Investigating Anti-diarrheal Phytochemicals in Vaccinium Corymbosum L., Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Led by investigator Mukundjee Pandey, the research team from Centurion University of Technology and Management explored the anti-diarrheal properties of phytochemicals derived from Vaccinium corymbosum L. The study utilized SDF files obtained from the Mol-Instincts, the CC-DPS database system, to analyze the interaction energies of various phytochemicals with the shikimate dehydrogenase enzyme of Escherichia coli. This data was crucial for identifying caffeic acid and aloe-emodin as potent inhibitors, thereby elucidating their role in disrupting the microbial metabolic cycle. Full Article

• Phytochemical Analysis of Cardamom Against Streptococcus Pneumoniae, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Led by investigator Dipankar Bhattacharyay, the research team from Centurion University of Technology and Management utilized molecular docking methods to identify effective phytochemicals in Cardamom against Streptococcus pneumoniae. The team used SDF files of phytochemicals downloaded from the Mol-Instincts, the CC-DPS database system, for their analysis. This data significantly contributed to identifying cinnamaldehyde and acetic acid as key compounds, inhibiting the vital thymidine phosphorylase enzyme of the microbe. Their findings offer a theoretical basis for the medicinal use of Cardamom in treating bronchitis. Full Article

• Phytochemical Analysis of Cardamom Against Mycoplasma Pneumonia, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  The research team led by Bapujee Palai from Centurion University of Technology and Management conducted a study on the phytochemicals in cardamom that can inhibit Mycoplasma pneumonia, which causes bronchitis. Utilizing the Mol-Instincts, the CC-DPS database system, they obtained SDF files of key phytochemicals for molecular docking studies. The study identified that acetic acid and cinnamaldehyde interact strongly with the glycerophosphodiester phosphodiesterase enzyme, disrupting the bacteria's life cycle and demonstrating the potential therapeutic effects of cardamom extracts. This research contributed to exploring the potential of cardamom in bronchitis treatment. Full Article

• Analysis of Cardamom Phytochemicals Against Tuberculosis, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Led by investigator Dipankar Bhattacharyay, the research team from Centurion University of Technology and Management used molecular docking techniques to study cardamom-derived phytochemicals against Mycobacterium tuberculosis. The team utilized SDF files obtained from the Mol-Instincts, the CC-DPS database system, to analyze phytochemicals such as acetic acid and 4-terpineol. This data was instrumental in determining the inhibitory potential of these compounds on the histidinol dehydrogenase (H37Rv) enzyme, providing key insights into the potential use of acetic acid as an effective agent against tuberculosis. Full Article

• Evaluating Coconut Phytochemicals for Antifungal Properties, Published in the EUROPEAN JOURNAL OF MEDICINAL PLANTS

  Investigator Sunanya Das from Centurion University of Technology and Management led a study analyzing the interaction of coconut phytochemicals with enzymes from Candida tropicalis, responsible for candidiasis. The research utilized molecular docking data from the Mol-Instincts, the CC-DPS database system, specifically focusing on SDF files for compounds such as folic acid and catechins. These compounds demonstrated significant inhibition of the 2-enoyl-CoA hydratase enzyme, crucial for the pathogen's metabolism, highlighting their potential as natural antifungal agents. This study underscores the importance of plant-based compounds in medicinal chemistry. Full Article

Pharmaceutical Nanotechnology

• Synthesis and Characterization of pH-Responsive Drug Delivery Nanoparticles, Published in the NANOFABRICATION

  Led by investigator Pierre P. D. Kondiah, the research team from the University of the Witwatersrand utilized the Chitosan (CHT) mol file from Mol-Instincts, the CC-DPS database system, to develop a Eudragit-Chitosan nanosystem for transporting Duloxetine to the brain. This research aimed to develop a nanoparticle system capable of protecting Duloxetine and releasing it in a controlled manner within the body. By combining Chitosan and Eudragit, the team created a cationic surface to enhance cell recognition and uptake, and increased the thermal stability of the nanoparticles. This data significantly enhanced the study's ability to predict and optimize the drug's behavior within the nanoparticle matrix. Full Article

Pharmacognosy

• Determination of Absolute Configuration of β-Eudesmol from Warionia Saharae Essential Oil, Published in the PHARMACOGNOSY

  Led by investigator Mimouna Yakoubi, the research team from the University of Bechar and University Mustapha Stambouli of Mascara utilized chemical property information from the Mol-Instincts, the CC-DPS database system, to confirm the absolute configuration of β-Eudesmol using Vibrational Circular Dichroism (VCD) spectroscopy. The comparison between experimental VCD spectra and Mol-Instincts chemical property information significantly contributed to verifying the stereochemistry of the isolated compound. These findings are crucial for understanding the pharmacological potential of β-Eudesmol. Full Article

• Phytochemical Inhibition of Entamoeba Histolytica, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Led by investigator Rukmini Mishra, the research team from Centurion University of Technology and Management conducted a study on the effectiveness of Bixa orellana-derived phytochemicals against Entamoeba histolytica. They utilized molecular data from the Mol-Instincts, the CC-DPS database system, specifically the SDF files of various phytochemicals, to determine their interactions with the alcohol dehydrogenase enzyme. The results indicated that benzoic acid, acetic acid, phenol, and anthraquinone showed significant inhibition of the enzyme, thus disrupting the life cycle of the pathogen. This data was crucial in supporting the theoretical basis of the medicinal properties of Bixa orellana against dysentery. Full Article

Pharmacology

• Assessing the Anti-Cancer Potential of Silymarin through Molecular Docking Studies, Published in the CUKUROVA MEDICAL JOURNAL

  Led by investigator Sümeyra Çetinkaya, the research team from the Biotechnology Research Center, Field Crops Central Research Institute in Ankara, Türkiye, explored the drug repurposing potential of silymarin for hepatocellular carcinoma (HCC). The team utilized silymarin structure data from the Mol-Instincts, the CC-DPS database system, to conduct molecular docking studies, evaluating its interaction with key proteins in the WNT/β-catenin signaling pathway. The study revealed that silymarin has a high binding affinity to the APC protein, indicating its potential to inhibit pathway overactivation, crucial for HCC treatment. This research provides significant foundational data for assessing the anticancer potential of silymarin. Full Article

• Evaluating Hepatoprotective Properties of Bael Leaves Using In Silico Analysis, Published in the INDIAN JOURNAL OF NATURAL SCIENCES

  Led by investigator Shakti Swarupa Pattanaik, the research team from Centurion University of Technology and Management used SDF files from the Mol-Instincts, the CC-DPS database system, to analyze pharmacophores from Bael leaves. These files were crucial for docking studies against target proteins related to Hepatitis viruses. This approach helped identify potential hepatoprotective compounds, enhancing the accuracy and reliability of the study. Full Article

• Evaluation of Phytochemicals from Bhringraj for Anti-Pneumonia Activity, Published in the JOURNAL OF PHARMACEUTICAL RESEARCH INTERNATIONAL

  Debajani Tripathy, Chandana Adhikari, Mukundjee Pandey, and Dipankar Bhattacharayay from Centurion University of Technology and Management conducted a study to identify phytochemicals in Bhringraj that could treat pneumonia. Utilizing molecular docking techniques with Biovia Discovery Studio, they sourced phytochemical structures from Mol-Instincts, the CC-DPS database system. The data provided insights into the interactions between Bhringraj phytochemicals and bacterial enzymes, revealing that glutamic acid had significant inhibitory effects on the pathogen. This work underscores the potential of database-driven research in identifying effective treatments for infectious diseases. Full Article

Physical Chemistry

• Analyzing the Thermodynamic Coefficients of Water and Argon, Published in the UKRAINIAN JOURNAL OF PHYSICS

  Led by researchers L.A. Bulavin and Ye.G. Rudnikov from Taras Shevchenko National University of Kyiv and the National Technical University of Ukraine, the study investigated the temperature and chemical potential dependencies of the thermodynamic coefficient for liquid water and argon. The researchers utilized physical property values from the Mol-Instincts, the CC-DPS database system, to calculate these dependencies. This study identified unique behaviors of water's thermodynamic coefficient at specific temperatures and chemical potentials and compared them to the properties of argon, highlighting water's distinct characteristics. Full Article

• Influence of Surfactant Chain Length on Oil Droplet Motion, Published in the COLLOIDS AND SURFACES A: PHYSICOCHEMICAL AND ENGINEERING ASPECTS

  Ben Nanzai and his team from Shizuoka Institute of Science and Technology investigated the spontaneous motion of oil droplets in aqueous solutions of trimethyl alkyl ammonium surfactants. This study utilized electron affinity data from the Mol-Instincts, the CC-DPS database system, to understand and analyze the droplet motion mechanism. The research found that the running speed of droplets increased with the length of the surfactant chain. These data contributed significantly to predicting and optimizing the efficiency of droplet motion. Full Article

• Theoretical Evaluation of Hexazinane as a Nitrogen-Rich Energetic Component, Published in the MOLECULAR SYSTEMS DESIGN & ENGINEERING

  Dr. S. V. Bondarchuk from Cherkasy National University conducted research on hexazinane as a potential nitrogen-rich energetic onium salt. The study mentioned the Mol-Instincts, the CC-DPS database system, which includes information on hexazinane and its derivatives. The research involved predicting the crystal structure and evaluating the chemical reactivity of hexazinane to investigate its potential as a high-energy density material. The findings highlight the suitability of hexazinane for high-performance propellant applications. Full Article

Plant Biotechnology

• In-silico Analysis of Methi Extract's Impact on Plant Disease, Published in the INDIAN JOURNAL OF NATURAL SCIENCES

  Investigator Preetha Bhadra and her research team at the Centurion University of Technology and Management conducted an in-silico analysis to evaluate the effects of Methi extract on plant disease, specifically targeting Aster Yellow. The study utilized pharmacophore data from the Mol-Instincts, the CC-DPS database system, including SDF files of various Methi compounds, which were instrumental in the docking studies against disease-causing microbial genes. This data was crucial in identifying compounds with significant docking scores, providing insights into potential natural treatments for plant diseases. These findings were published in their comprehensive study on the subject. Full Article

Polymer Chemistry

• Enhancing the Mechanical Properties of 3D-Printed Polymers, Published in the TECHNOLOGIES

  John Ryan C. Dizon of Bataan Peninsula State University and his collaborators utilized structural data of PETG from the Mol-Instincts, the CC-DPS database system. This data helped predict and enhance the crystallization kinetics of polyetheretherketone (PEEK) matrices. By comparing these predicted structures with experimental data, the team achieved significant improvements in the mechanical properties and dimensional stability of 3D-printed polymers. The findings provide a robust methodology for future enhancements in 3D printing technology, contributing to more reliable and durable polymer products. Full Article

• Simulation of Thermoset Polymer Properties Using Molecular Dynamics, Published in the MACROMOLECULES

  Led by investigator Erik E. Santiso, the research team from North Carolina State University utilized data from the Mol-Instincts, the CC-DPS database system, to parameterize coarse-grained models for molecular simulations. The study focused on modeling the reaction kinetics, structure development, and properties of thermoset polymers using a top-down coarse-graining strategy. Mol-Instincts provided crucial data that simplified the process of force field development, leading to simulation results that closely matched experimental observations. This data assisted in understanding network formation and property development of polyester-polyol resins used in coatings. Full Article

• Modeling Glass Transition Temperatures of Polyester Polyols, Published in the NORTH CAROLINA STATE UNIVERSITY PROQUEST DISSERTATIONS & THESES

  Researchers at North Carolina State University parameterized the SAFT-γ Mie force field using critical temperature (Tc), acentric factor (ω), and liquid density at 0.7 Tc from the Mol-Instincts, the CC-DPS database system, for triazinane trione and hexyl isocyanate. This data played a crucial role in predicting the glass transition temperatures (Tg) of polyester polyols. The study developed an accurate model for predicting the glass transition temperature, contributing to the analysis and optimization of the physical properties of polyester polyols. Full Article

• Molecular Modeling of Aspartame with Molecularly Imprinted Polymers, Published in the NAMIK KEMAL UNIVERSITY INSTITUTIONAL REPOSITORY

  The research conducted by Yunus Sevindik at Namık Kemal University focused on the molecular modeling and synthesis of molecularly imprinted polymers (MIPs) using aspartame as a template. The study employed Gaussian 09W software for quantum chemical calculations to determine the optimal geometries and thermodynamic parameters of aspartame and ortho-phenylenediamine. During this process, the structure from the Mol-Instincts, the CC-DPS database system, was referenced for 3D modeling. These data facilitated the design and synthesis of effective MIPs, which were tested for their selectivity and binding affinity to aspartame. The study concluded that these computational insights play a crucial role in MIP development. Full Article

Theoretical Chemistry

• Comprehensive Analysis of [1,1'-Bicyclopropyl]-2-Octanoic Acid, 2'-Hexyl-, Methyl Ester Using DFT, Published in the INTERNATIONAL JOURNAL OF SCIENTIFIC AND TECHNOLOGY RESEARCH

  Researchers S. Sathish, P. Rajesh, A. Kala, R. Gopathy, and P. Kandan from Vels Institute of Science Technology & Advanced Studies and Annamalai University conducted an in-depth study on [1,1'-Bicyclopropyl]-2-octanoic acid, 2'-hexyl-, methyl ester. They utilized information from the Mol-Instincts, the CC-DPS database system, to perform Density Functional Theory (DFT) calculations with the B3LYP/6-311++G(d,p) basis set. The study revealed significant correlations between experimental and calculated UV-Vis spectra, HOMO-LUMO energies, and molecular electrostatic potential (MEP) mappings, providing a comprehensive understanding of the molecule's reactivity and stability. This data was crucial for validating theoretical models against experimental results. Full Article

• Role of Ligand Straining in Complexation of Eu³⁺ and Am³⁺ Ions, Published in the ACS OMEGA

  Investigator Sk. Musharaf Ali at the Bhabha Atomic Research Centre conducted a study on the complexation of Eu³⁺ and Am³⁺ ions with TPEN and PPDEN ligands using density functional theory (DFT) with the COSMO-RS solvation model. They utilized the optimized structure data of PPDEN from the Mol-Instincts, the CC-DPS database system, to analyze the selectivity and efficiency of these ligands. This data significantly contributed to understanding the ligand straining effects and the thermodynamics of the complexation process, which are crucial for nuclear waste reprocessing. Full Article

Thermodynamics

• Phase Diagrams and Thermodynamic Analysis of Water Isotopologues and Noble Substances, Published in the UKRAINIAN JOURNAL OF PHYSICS

  Led by investigators L.A. Bulavin, Ye.G. Rudnikov, and S.O. Samoilenko from Taras Shevchenko National University of Kyiv and Poltava State Medical University, this study utilized entropy and enthalpy data from the Mol-Instincts, the CC-DPS database system, to analyze the phase diagrams of water isotopologues and noble gases. The research demonstrated the applicability of the principle of corresponding states and provided critical insights into the thermodynamic behavior of these substances. The study focused on comparing and verifying various thermodynamic properties, including phase transitions of water and predicting phase diagrams of noble gases. Full Article

• Analyzing the Thermodynamic Coefficients of Water and Argon, Published in the UKRAINIAN JOURNAL OF PHYSICS

  Investigators L.A. Bulavin, Ye.G. Rudnikov from Taras Shevchenko National University of Kyiv and the National Technical University of Ukraine conducted a comparative study on the thermodynamic coefficients of water and argon. Utilizing the Mol-Instincts, the CC-DPS database system, they derived temperature dependences for various thermodynamic properties. The data provided by Mol-Instincts were used to predict the temperature dependence of the density and pressure for the liquid phase, aiding in the analysis of water's anomalous behaviors. Their findings contribute significant insights into the thermodynamic properties of water, particularly in identifying unique temperature points where these properties exhibit peculiar behaviors. Full Article

Thermodynamics and Molecular Simulation

• Parametrization of Homonuclear Ring Compounds, Published in the LANGMUIR

  Led by investigator Erich A. Müller from Imperial College London and Andrés Mejía from Universidad de Concepción, the research team employed thermodynamic properties data from the Mol-Instincts, the CC-DPS database system. This data helped accurately fit the SAFT-VR Mie equation of state parameters to experimental values of critical temperature, acentric factor, and liquid density for various ring compounds. The Mol-Instincts data enabled the researchers to validate their model through comparison with experimental results, significantly enhancing the predictive accuracy of phase equilibria and interfacial tensions in molecular simulations. These findings contribute to a deeper understanding of the phase behavior of complex molecular systems. Full Article

Virology

• Hemagglutination Mechanism of SARS-CoV-2 Spike Protein and Its Inhibition, Published in the INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

  Conducted by David E. Scheim of the US Public Health Service, this study involves an in-depth analysis of hemagglutination mediated by the SARS-CoV-2 spike protein. The research utilized the three-dimensional structure of Ivermectin from the Mol-Instincts, the CC-DPS database system, to analyze the binding mechanism of Ivermectin to the viral spike protein. This data played a crucial role in understanding the interaction mechanisms between the viral spike protein and red blood cell surface sialoglycoproteins, aiding in the identification of potential therapeutic agents. The findings underscore the importance of computational data in elucidating the biochemical pathways of COVID-19. Full Article