Articles tagged with Drug Discovery
The European capacity for antibiotic research and development requires sustained investment to combat growing resistance. Collaboration and risk-sharing can help keep companies in anti-infective drug development, as a temporary funding strategy may lead to lost efforts.
Researchers developed a label-free biological sensing method that can detect substances at the zeptomolar level, significantly improving drug testing and research capabilities. This advancement has the potential to lead to portable sensors for environmental toxins, food quality monitoring, and cancer screening.
Researchers at UCSF develop a method to target GTPases, enzymes involved in Parkinson's and many other diseases, by using drugs targeting the K-Ras oncogene. This approach reveals new drug binding sites that could not be predicted by computational tools.
Researchers investigated peptide clumping behavior using molecular dynamics simulations and AI techniques. They discovered that aromatic amino acids enhance aggregation, while hydrophilic ones inhibit it, offering insights into peptide structure and function.
Researchers have developed a new method to increase speed and success rates in drug discovery by combining data from the Library of Integrated Network-based Cellular Signatures with targeted docking simulations. This approach can accelerate the drug research process, identifying potentially effective compounds more efficiently.
A University of Oklahoma researcher has been awarded a NIH grant to evaluate thousands of natural products with therapeutic potential. The goal is to identify specific components of these products that have anticancer properties and understand how they work.
Researchers successfully put tadpoles into a hibernation-like state using FDA-approved donepezil (DNP), which could be repurposed to save millions of lives every year. The drug induces torpor-like symptoms and can be easily administered, making it a promising tool for emergency situations.
Researchers at Purdue University have received an additional $95,000 from the Trask Innovation Fund to further de-risk their technology for patent-pending drought-resistant soybean plants. Herman Sintim's team aims to develop novel compounds that potentially inhibit TAK1, a protein kinase shown to play key roles in arthritis.
Using Explainable AI (XAI), researchers analyzed AI model predictions for antibiotic candidates, identifying critical molecular structures and variables. This helped improve predictive models, which can now be trained on what's truly important for activity.
A wearable sensor supported by machine learning models can continuously monitor and quantify FOG episodes, providing an accurate picture of a patient's condition. This technology has the potential to support the development of new treatments and improve the lives of people with Parkinson's disease.
Researchers developed a novel EIT-EVA PCB sensor for non-invasive assessment of drug inhibition on ion channels. The system enables real-time monitoring of ion flow changes in response to drug exposure, offering a faster and more efficient alternative to traditional methods.
Researchers at Ben-Gurion University of the Negev have developed a new compound, PL-18, which disrupts bacterial quorum sensing and biofilm formation. This compound has shown promise in reducing bacterial virulence and inhibiting iron uptake, suggesting potential applications in combating antibiotic-resistant bacteria.
A rhodium-catalyzed [2+2+1] cycloaddition reaction expands the possibilities for creating complex organic molecules. The researchers achieved high enantiomeric excess values of 94-99% using phosphine ligands, enabling the synthesis of diverse compounds.
Researchers found that rotifers acquire genes from bacteria and produce resistance weapons, such as antibiotics and antimicrobial agents. The team's findings suggest that rotifers could give important clues in the hunt for new antibiotics to treat human infections.
A team of UCSF specialists predicts 24-hour seizure risk using brain activity patterns that foreshadow seizures. The discovery may improve quality of life for 2.9 million Americans living with epilepsy.
Researchers at Hokkaido University have developed a comprehensive derivative synthesis method to find new antimicrobial drugs. They identified eight analogs possessing strong MraY inhibitory and antibacterial activity, with one showing promising effectiveness in mouse infection models.
Researchers at the University of Cambridge have developed an atlas of proteins that reveals how they behave inside human cells. The tool allows for the identification of new proteins involved in important bodily functions, including fat distribution and protein creation.
The University of Texas at Arlington's Junha Jeon is developing transition metal-free cross-coupling technologies using arynes to deliver medications safely and effectively. This project aims to improve the production of drugs, particularly for cancer treatment, by reducing impurities left behind by metals.
A study found that targeting heparan sulfate-modified proteins improves cell repair, rescues neuron loss and reverses cellular changes associated with neurodegenerative diseases. Disrupting these proteins promotes autophagy-dependent cell repair and reverses early cellular problems in models of Alzheimer's.
TopoFormer uses a transformer model trained on tens of thousands of protein-drug interactions to recreate 3D structures as one-dimensional information that current models can understand. This enables more accurate predictions of new drug interactions and potentially reduces development time and costs.
Researchers at EPFL have created a deep learning pipeline to design soluble analogues of cell membrane proteins, making them easier to study and use in pharmaceutical development. The approach has shown remarkable success in producing functional proteins that maintain parts of their native functionality.
Scientists have identified a mechanism that enables enzymes to communicate and produce organic molecules with disease-fighting properties. This breakthrough could aid in the discovery of new drugs by allowing researchers to design or modify enzymes to create novel natural products.
PSICHIC uses sequence data and AI to decode protein-molecule interactions with state-of-the-art accuracy, eliminating costly processes like 3D structures. The tool effectively screens new drug candidates and performs selectivity profiling, offering a more efficient and reliable approach to drug discovery.
Scientists have created mirror-image cyclodextrins in the laboratory, which could make it easier to formulate and deliver complex medications. These discoveries may also lead to improved treatment of cardiovascular diseases caused by atherosclerotic plaques.
Researchers developed Epitope Binning-seq to analyze epitopes in monoclonal antibodies. The method accurately classified antibodies into distinct epitope bins, providing valuable insights into their binding patterns and streamlining early antibody drug development.
Researchers have developed a route to modify peptides to target disease diagnostics and drug discovery, focusing on protein-protein interactions (PPIs). By modifying a small peptide sequence, the team showed it binds more quickly and strongly to specific PPI targets.
A study led by Washington State University scientists found that inhibiting CDK7 could help prevent heart damage associated with doxorubicin, a commonly used cancer chemotherapy medication. The researchers also discovered that CDK7 inhibition enhances the medication's cancer-killing capability.
Researchers at Texas A&M University developed vessel-chip technology to create a platform for preclinical drug discovery, reducing the need for animal testing. The system mimics human circulatory systems using tissue-engineered microfluidic devices.
Researchers at Okayama University developed a novel AAK1 inhibitor using Kinobeads technology, shedding light on its inhibitory mechanism and targeting various neurological disorders and viral infections. This breakthrough paves the way for rapid and cost-effective enzyme inhibitors with clinical applications.
The latest SLAS Technology issue highlights recent breakthroughs in skin cutaneous melanoma, glycan-bead coupling, and acoustic ejection mass spectrometry. Researchers adapt technological advancements for life sciences exploration and experimentation in biomedical research and development.
Researchers develop an in vitro model to study tau aggregation, a process linked to neurodegenerative diseases like Alzheimer's and frontotemporal dementia. The approach offers a short timeline for generating data and facilitates the study of potential therapeutic interventions.
Dr. George Bray's paper tracks the development of obesity research over 100 years, from early explorations to modern medication interventions. The study highlights the chronic and relapsing nature of obesity as a disease process.
Nach0 was trained on diverse tasks, including natural language understanding, synthetic route prediction, and molecular generation. The model performed well on molecular tasks using molecular data and outperformed ChatGPT, making it a significant step toward unlocking the full potential of LLMs for drug discovery.
A recent study by Bryan Roth and colleagues validated the accuracy of AlphaFold2 in modeling ligand binding sites, leading to promising results for drug discovery. The researchers found that up to 54% of potential compounds interacted successfully with the target proteins, paving the way for new treatments.
Insilico Medicine's lead compound demonstrates strong enzymatic activity, selectivity, and favorable ADME properties, as well as antitumor activity in various animal models. The company's generative AI-powered platform generated over 3,600 candidate molecules before identifying the promising lead compound.
Researchers at UC San Diego developed a machine learning algorithm to simulate chemistry in drug discovery, synthesizing 32 potential multi-target cancer drugs. The new AI platform, POLYGON, can identify molecules with multiple targets, potentially delivering more effective treatments with fewer side effects.
Researchers at Insilico Medicine developed COSMIC, a new framework for molecular conformation space modeling that provides accurate insights into molecule positioning and activity. This enables faster and more efficient drug design decisions.
Researchers at CeMM and Pfizer have developed a novel method to measure the binding activity of hundreds of small molecules against thousands of human proteins. The study revealed tens of thousands of ligand-protein interactions that can now be explored for drug development.
Researchers at CeMM Research Center create 'vpCells' method for simultaneous fluorescent labelling of many proteins, enabling precise tracking and exploration of protein function. The approach opens up new applications in fundamental cell biology and drug discovery.
The University of Birmingham's Drug Discovery Hub has developed a novel approach to drug discovery, enabling projects to overcome the funding gap between original research and commercial investment. The hub has attracted over £4m in industry funding, grants, and awards, with a successful portfolio of projects.
Researchers at Insilico Medicine have identified a new class of Polθ inhibitors featuring central scaffolding rings, designed using Chemistry42, with significant enzymatic and cellular potency. The discovery showcases the potential of AI in medicinal chemistry for precise molecular modifications.
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.
The KEDD framework integrates structured and unstructured knowledge to enhance predictive accuracy in AI-driven drug discovery. It outperforms existing models in critical tasks, including the 'missing modality problem', by leveraging sparse attention and modality masking techniques.
Researchers develop AI-powered method to rapidly predict multiple protein configurations, understanding protein dynamics and functions. This breakthrough has the potential to revolutionize drug discovery by uncovering more targets for new treatments.
Researchers have developed a new, synthetic lung surfactant that mimics the functionality of animal-derived formulations. The surfactant has shown promise in reducing surface tension and may offer a cheaper alternative to Infasurf.
A newly developed compound is showing promise as a more effective treatment for human schistosomiasis, an understudied tropical disease caused by parasitic worms. The compound overcomes the limitations of current treatment praziquantel by being effective against the larval stage and resistant strains.
Researchers at the University of Toronto and Sinai Health have created a new platform to identify proteins that can be co-opted to control the stability of other proteins. The study identified over 600 new effector proteins that could be used therapeutically, including those that can efficiently degrade or stabilize target proteins.
Researchers at Xi'an Jiaotong-Liverpool University developed a new method that enables the efficient production of cysteine-rich peptides and microproteins in their naturally folded 3D structure. The approach uses organic solvents to mimic nature's oxidative folding process, resulting in speeds of over 100,000 times faster than aqueous...
Researchers have identified a potential path to eliminate the viral reservoir that prevents people from being completely cured of HIV. A new drug candidate, called a proteolysis targeting chimeras (PROTAC) molecule, triggers the degradation of the Nef protein, which suppresses HIV replication and restores immune system detection. This ...
Researchers have discovered compounds that can activate estrogen-related receptors (ERRs), similar to the effects of exercise on metabolism and muscle growth. The new compounds may offer a substitute for exercise in people with medical conditions, such as heart failure and neurodegenerative disease.
Researchers have identified a subset of T-cells that acts like stem cells and continuously generates effector T-cells that attack transplanted organs. Targeting the transcription factor IRF4 may lead to innovative therapies for patients with chronic infections, cancers, autoimmune diseases and transplanted organs.
A new study suggests that mushroom extract containing psilocybin demonstrates superior efficacy in stimulating neuroplasticity and promoting new connections between nerve cells. The research found that the extract had a more potent and prolonged impact on synaptic plasticity compared to chemically synthesized psilocybin.
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 University at Buffalo propose a new approach to developing cancer drugs by determining the optimal placement of molecular linkers earlier in the process, reducing trial and error and increasing potency, according to a study published in Communications Chemistry.
A network of African scientists has secured funding to develop new drugs for malaria and TB, two major diseases affecting the continent. The Grand Challenges Africa Drug Discovery Accelerator Programme will support leading research efforts on these diseases.
Researchers at City of Hope have developed a new approach to target and destroy hard-to-kill leukemia stem cells. The therapy method uses Type II interferon to disrupt the cancer cells' ability to divide, and a T cell engager antibody to create a bridge between the immune system and the leukemia stem cells.
PandaOmics uses advanced AI algorithms to process vast quantities of diverse data, performing gene and pathway analysis and target predictions. The platform has been extensively validated in multiple therapeutic areas, including oncology, inflammation, and immunology.
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 North Carolina State University are developing a suite of performance metrics to standardize the evaluation of self-driving labs in chemistry and materials science. These metrics aim to compare different lab technologies and identify areas for improvement, ultimately advancing the field and accelerating discovery.
A new AI tool, DeepGO-SE, successfully predicts the molecular functions of unknown proteins with high accuracy. This breakthrough enables researchers to analyze uncharacterized proteins, facilitating tasks such as drug discovery, metabolic pathway analysis, and disease associations.