Articles tagged with Computational Chemistry
The study identifies a new area where a correction for the self-interaction error breaks down, allowing researchers to pinpoint flaws and develop solutions. By refining DFT, scientists can design better catalysts, leading to improvements in fields such as food production and technology.
Insilico Medicine has discovered a potent and selective CDK12/13 dual inhibitor, compound 12b, using AI-powered platforms. The compound shows significant efficacy in breast cancer and AML models without inducing side effects, paving the way for further therapeutic development.
Researchers created new proteins using AI that bind to and neutralize deadly snake toxins, providing a safer alternative to traditional antivenoms. The study's results show an 80-100% survival rate in mice, offering potential benefits for people in developing countries.
Researchers have made a breakthrough in decoding the growth process of Hexagonal Boron Nitride (hBN), a 2D material with unique versatility. The findings reveal the formation of nanoporous hBN, expanding its potential environmental applications, including sensing and filtering pollutants.
A new study uses machine learning to reduce time needed for calculating screening parameters in Koopmans functionals, enabling faster predictions of material spectral properties. Researchers trained a simple model using modest data and achieved accurate results, paving the way for studying temperature-dependent spectral properties.
Researchers at Chiba University have created an electronically controllable sliding molecular machine using a newly modified ferrocene molecule. The discovery overcomes the challenge of stabilizing the fragile ferrocene molecule on a flat surface, enabling precise control of its motion through electrical signals.
Researchers at Colorado State University have developed a new method to break down PFAS, a group of human-made 'forever' chemicals. The system uses an LED light-based photocatalytic approach that can be used at room temperature, offering a more sustainable and efficient solution than traditional chemical manufacturing processes.
Chris Van de Walle, a distinguished professor at UCSB, has been awarded the American Physical Society's 2025 Aneesur Rahman Prize for Computational Physics. He was recognized for his development and application of first-principles methods to compute structural, electronic, and optoelectronic properties of point defects and interfaces.
Researchers at Wayne State University are developing new AI-powered methods to design and develop new drugs, including carbohydrate-based treatments for cancer. The study aims to improve the accuracy of simulations and machine learning models to predict the behavior of complex biological molecules.
Computational insights from ICIQ researchers apply Marcus theory to estimate free-energy barriers in energy transfer processes, enabling the prediction of EnT barriers and facilitating advances in photocatalysis. The asymmetric variant of the Marcus theory provides more accurate barriers for sensitization of alkenes.
A team of scientists successfully interfaced AiiDA with the Alps supercomputer, completing over 100,000 calculations in just 16 hours. The run demonstrated the maturity of Swiss-made software tools for computational materials science and showcased the power of Switzerland's main supercomputing facility.
A study led by Cesar de la Fuente and his team has uncovered sequences for infection-fighting molecules in the genomic data of extinct species. These ancient β-defensins may lead to the creation of new antimicrobial therapies, including antibacterial, antifungal, and antiviral treatments.
A new structure of light has been discovered that can accurately measure chirality in molecules, a property of asymmetry important in physics, chemistry, biology, and medicine. This 'chiral vortex' provides an accurate and robust form of measurement, allowing for the detection of chiral biomarkers.
Researchers at the University of Illinois have developed a method to understand and improve light-harvesting molecules for solar energy applications. By combining AI with automated chemical synthesis and experimental validation, they were able to produce molecules four times more stable than traditional ones.
A research team led by Magnus Wolf-Watz at Umeå University has discovered the chemistry behind the cell's energy molecule ATP. They found that a small angle change caused by magnesium can significantly speed up the chemical reaction producing ATP, linking structure and catalytic effect.
Researchers developed a new computational methodology to simulate polyoxometalate (POM) formation, enabling the prediction of key factors and suitable conditions. This enhances the open-source tool POMSimulator, facilitating efficient processing of numerous speciation models.
Physicists at European XFEL have made comprehensive observations of ionisation processes in warm dense matter. The team observed how quickly copper transforms into the exotic state of ionised WDM to become transparent to X-rays.
Researchers develop new method to create azetidines, a stable form of heterocycle that don't oxidize under physiological conditions, expanding toolkit for drug developers to improve medication safety and reduce side effects. This breakthrough enables the creation of more useful chemical transformations with better medical functions.
Researchers have developed a novel technique using a new holmium catalyst for synthesizing hydrocarbazoles with tetrasubstituted carbon. The method uses a lanthanide-based catalyst and can be recycled, paving the way for sustainable chemical processes.
Researchers at Osaka University have developed systematically designed molecules that absorb near-infrared light but not visible light, paving the way for new applications in electronics. The new compounds show promise in areas such as solar cells, transistors, chemotherapy, and photodetectors.
A team of researchers from Tokyo Institute of Technology has developed a new type of computational RNA droplet that can perform logical AND operations using microRNA sequences. These programmable droplets have the potential to be used in various applications, including biomolecular sensing and artificial cells.
Researchers develop LoCoHD algorithm to compare protein structures based on chemical information of atoms, enabling analysis of molecular machines and identifying critical amino acids. The method shows promise in predicting protein functions, including studying the internal motion of proteins like podocin.
Researchers discovered that just eight new biochemical reactions can bridge the gap between simple geochemistry and biochemistry, indicating a limited loss of biochemistry to time. This finding suggests that even extinct reactions can be rediscovered from clues left behind in modern biochemistry.
The new materials offer an alternative to metal-organic frameworks (MOFs) and have already shown early promise for the capture of iodine. They are yet to be fully explored but hold potential for applications in proton conduction, catalysis, water capture, and hydrogen storage.
A Japanese research team has developed a framework that accurately describes how first-order reactions appear depending on the time interval used to measure the reaction. The work uses a 'shutter speed' analogy to simplify complex molecular changes, allowing for precise predictions of reaction outcomes.
Researchers at Yokohama National University utilized machine learning and AI to predict the selectivity of chemical reactions. By analyzing molecular factors such as sterics and orbitals, they developed a method to better understand reaction mechanisms, leading to more efficient synthesis of desired products.
Researchers developed ChemCrow, an AI-powered tool that integrates expertly designed software tools to autonomously perform chemical synthesis tasks. The system enables plan-and-execute approach with reduced hallucinations and practical application, accelerating research and development in pharmaceuticals and materials science.
A team of researchers from Osaka University used machine learning to identify a highly effective boron-based catalyst for chemical transformations of amino acids and peptides. The new catalyst generates only water as a coproduct and promotes high-yield reactions with minimal environmental impact. By leveraging computational methods, th...
A new AI method developed by Swedish researchers can identify toxic substances based on their chemical structure, potentially replacing animal testing. The method has been shown to be more accurate and broadly applicable than existing computational tools, offering a promising alternative for environmental research and authorities.
Researchers integrated AiiDA with tomato to control battery cycling, allowing for automated experiments and data collection. The integration enables batch submission of protocols, provenance tracking, and analysis.
Researchers studied triphenylphosphine on graphite and discovered it moves with surprisingly little energy, jumping and rotating like a spacecraft. This insight holds potential for future nanotechnologies, including advanced materials and more efficient ways of making medicines.
A new statistical-modeling workflow can quickly identify molecular structures of products formed by chemical reactions, accelerating drug discovery and synthetic chemistry. The workflow also enables the analysis of unpurified reaction mixtures, reducing time spent on purification and characterization.
A research team from Doshisha University discovered a novel phenomenon for generating self-organized characteristic patterns through phase separation of polymer solutions in glass capillary tubes. They created uniform microdroplets containing DNA and medicines, which maintained their alignment for eight hours, offering insights into bi...
Elfi Kraka and colleagues develop a novel machine learning interatomic potential called ANI-1xnr that accurately simulates atomic-level interactions in various environments. The model has the potential to aid in understanding planetary care, drug interactions and exploring cosmic materials.
Researchers used generative AI to design a lead molecule for treating fibrosis, a biological process associated with aging. The compound, INS018_055, demonstrated significant efficacy in preclinical studies and showed promising results in clinical trials, accelerating drug discovery and providing new therapeutic options.
Researchers at Carnegie Mellon University have created a new machine learning model that can simulate reactive processes in diverse organic materials and conditions. The model, called ANI-1xnr, performs simulations with significantly less computing power and time than traditional quantum mechanics models.
Researchers used mass spectrometry imaging and single-cell metabolomics with deep learning to create 3D molecular maps of the brain, enabling a better understanding of chemical interactions within brain tissue. This breakthrough could help address currently intractable neurological diseases.
Researchers at the University of Pittsburgh have developed a small-scale system that forms three-dimensional patterns, which serve as chemical fingerprints that allow chemicals in solutions to be identified. The system utilizes fluid flows and flexible posts coated with enzymes to generate distinct visual patterns.
Scientists from IOCB Prague have developed a universal and accurate new computational method to predict how proteins interact with drugs. The SQM2.20 scoring function yields DFT-quality predictions in minutes, significantly accelerating drug discovery.
Researchers at UNSW Sydney have successfully encoded quantum information in four distinct ways using a single antimony atom. This breakthrough enables more flexibility in designing future quantum computing chips, with each method offering unique advantages and potential trade-offs.
Researchers at the University of Liverpool have discovered a new solid material that rapidly conducts lithium ions, replacing liquid electrolytes in current battery technology. The discovery provides a platform for optimising chemistry to enhance material properties.
The UK's first academic-industrial institute dedicated to chemistry & AI research will bring together leading researchers and industry partners. The hub aims to develop state-of-the-art AI tools for chemistry, addressing key societal needs like net zero emissions and global health.
Researchers have discovered that magnetostriction causes a magnetic phase transition in manganese oxide at 118K, leading to the switch of muon sites. The study uses advanced simulations and resolves a long-standing puzzle, shedding new light on antiferromagnetic oxides.
Scientists at Linköping University have successfully developed molecular gears with controlled rotary motion, overcoming previous challenges of single bond rotation. This breakthrough paves the way for future applications in medical drug delivery and solar energy storage.
Researchers at Universiteit van Amsterdam developed an autonomous chemical synthesis robot with integrated AI, outperforming human chemists in terms of speed and accuracy. The 'RoboChem' system can perform various reactions while producing minimal waste and delivering results quickly.
Insilico Medicine has discovered a novel PHD inhibitor for treating anemia using its AI-powered generative chemistry platform Chemistry42. The compound demonstrated favorable ADMET and PK profiles in animal models, showing promise for further investigations.
Researchers have successfully transmitted a domino effect in redox reactions for the first time. The new mechanism involves a two-part molecule that undergoes structural changes upon oxidation, triggering further oxidation in neighboring groups. This discovery has potential applications in nanoscale computing and energy systems.
Researchers have developed a precise and efficient tool using 'single atom skeletal editing' to insert single carbon atoms into cyclic compounds, enabling ring size adjustment from five to six-membered rings. This approach opens up the way for designing and modifying complex molecular structures with potential industrial applications i...
Researchers have developed a novel method to produce a selective anticancer precursor substance. The synthesis involves the reaction of metal-active oxygen species with nitrile, utilizing cost-effective metals at lower temperatures. This breakthrough opens up new possibilities in developing innovative drugs against cancer.
A new NSF-supported collaboration aims to improve liquid organic hydrogen carriers and use AI to identify novel approaches for a global renewable energy supply chain. The team is developing a new class of molecules, chemistries, and chemical processes to better store and transport green energy across the globe.
Scientists have developed a new, efficient ethanol catalyst made from copper nanoparticles, which is cheaper than platinum and could increase the potential of ethanol fuel cells. The catalyst was created through laser melting and shows great promise for improving ethanol oxidation.
A University at Buffalo-led research team has developed molecules that can transport chloride ions across cell membranes, increasing airway surface liquid and restoring normal mucus clearance in cystic fibrosis cells. The synthetic anion binders offer a new potential treatment for the chronic disease.
Researchers at IOCB Prague have described the causes of azulene's blue color and its unusual properties, which can help capture and utilize light energy. The team used a simple concept to explain the molecule's behavior, opening up new possibilities for organic chemistry.
By controlling the arrangement of multiple layers within crystals, researchers can tune the materials' optoelectronic properties and emit light of specific energies. This technique has significant implications for applications such as LEDs, solar cells, and lasers.
The workshop, under Prof. Rafael Bernardi and Prof. Emad Tajkhorshid's guidance, showcased expertise from NAMD and VMD developers, providing in-depth dives into molecular dynamics simulations and biomolecular visualization.
Researchers from Japan have solved a long-standing puzzle of porous soft materials, revealing the importance of elastic heterogeneity in tuning molecular adsorption/desorption properties. The study provides physicochemical insight into the origin of elastic heterogeneity within MOFs, with applications to imparting targeted properties.
Researchers from Hokkaido University developed a centralized, interactive platform to explore and analyze chemical reaction pathways. The Searching Chemical Action and Network (SCAN) platform utilizes AFIR calculations to provide an interactive reaction pathway map that can be searched and viewed.
A team of researchers developed a novel computational approach to identify allosteric sites in integrins, revealing previously inaccessible druggable pockets. This breakthrough has the potential to overcome limitations in integrin-targeting medication and open new avenues for drug discovery.
Scientists at POSTECH successfully grow two-dimensional molecular crystals, demonstrating control over exciton interactions. The findings could enable various applications in organic semiconductors and solar power generation.
Researchers have developed a mononuclear iron complex that selectively and efficiently converts methane to methanol under mild conditions. The catalyst achieves high efficiency and selectivity due to a hydrophobic environment near the active iron center, trapping methane and releasing methanol.