Articles tagged with Computational Chemistry
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.
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.
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.
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 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 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 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.
Scientists conducted computational simulations to clarify the origin of life on Earth. The study suggests that chiral asymmetry may have originated in space through CP Lyman-α emission line, influencing the production of biological amino acids.
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.
Researchers from Osaka University developed an AI algorithm called FINDE that discovers and preserves the underlying conservation laws of real-world dynamical systems, not just superficial dynamics. FINDE allows for more accurate computer simulations and can reveal additional information about a system's structure.
The POSTECH team developed a multifunctional tip-enhanced spectroscopy that dynamically controls the physical properties of quasiparticles in 2D materials. This technology increases interlayer excitons' luminous efficiency by 9,000 times and modulates their energy.
Researchers at Colorado State University have created a synthetic molecule with an asymmetric oxygen atom that remains stable and nonreactive. This feat is significant because chiral molecules can have drastically different properties and are crucial in fields like drug discovery and materials engineering.
Researchers utilized the Chemistry42 platform to generate novel molecular structures and identified a hit molecule for CDK20, a promising target for hepatocellular carcinoma. The platform's customizable reward function and generative models enabled efficient design and optimization of molecules.
Researchers successfully applied AlphaFold AI to an end-to-end platform, discovering a novel target and developing a potent hit molecule for liver cancer. The study demonstrates the potential of AI-powered drug discovery to accelerate treatment development.
Researchers used auto-encoder technique to analyze 150 XRD patterns of magnetic alloys, identifying clusters and fine-tuning alloys by detecting relevant peaks. The approach enables accelerated development of high-efficiency materials with low environmental impact.
A Collaborative Research Centre investigates animal navigation using the Earth's magnetic field. The study focuses on vertebrates, including birds and fish, aiming to protect endangered migratory species.
Researchers developed a computational protocol called MACH that can quickly determine whether a given compound will form a crystal hydrate. The tool uses rules to systematically determine where water would likely be inserted into a crystal, providing essential knowledge for drug development and formulation.
Researchers from the Max Born Institute found that magnesium ions reduce ultrafast fluctuations in water's hydration shell, slowing solvation dynamics. The study reveals a short-range effect of individual ion pairs on dilute aqueous systems.
A search of the Cambridge Structural Database found nearly 1,800 conglomerate crystal structures with spontaneous enriched chirality, augmenting synthetic building blocks for medicinal chemists. This discovery introduces a new pool of chiral molecules outside of natural sources, potentially leading to more effective treatments.
Researchers at Hokkaido University used computer simulations to discover a reaction that selectively adds two fluorine atoms to a difficult-to-access position on an N-heterocycle. The successful synthesis of 48 new compounds with unique alpha position fluorine substitutions has significant potential for novel drug development.
Researchers from Aarhus and Berlin have developed an algorithm that can predict how complex molecules will bind to the surface of catalysts. This is achieved through a machine-learning approach inspired by 3D Tetris, allowing computers to quickly identify promising catalysts.
New evidence from Antarctica shows toxic fluorinated forever chemicals have increased markedly in recent decades, with scientists believing CFC-replacements are a likely source. The most abundant chemical discovered was perfluorobutanoic acid (PFBA), which has been linked to human exposure and immune system impairment.
The use of computational tools and new strategies is transforming drug discovery, enabling researchers to navigate the vast chemical space more efficiently. Companies are leveraging virtual libraries of compounds and advanced technologies like artificial intelligence and machine learning to optimize screens and identify promising leads.
A new double-layered catalyst, combining platinum with NiFe hydroxide, was developed to enhance hydrogen generation efficiency. The catalyst's activity is 11.2 times higher than conventional materials, making it a promising solution for increasing green hydrogen production.
Scientists found that certain dynamical defects help explain the allowed vibrational modes inside amorphous solids, like glasses. These findings may lead to controlling the properties of amorphous materials.
Researchers at the University of Pittsburgh have discovered a new catalyst, tungsten oxide, that can efficiently convert carbon dioxide into useful fuels and chemicals. This breakthrough could lead to a significant reduction in global warming by utilizing hydrogen produced from renewable energy sources.