Articles tagged with Drug Discovery
Researchers are developing long-acting formulations for malaria and TB prevention, as well as a single-injection cure for hepatitis C. The project aims to improve patient adherence and reduce disease transmission in low- and middle-income countries.
Researchers mapped protein-drug interactions in rat organs and blood, revealing potential drug targets. The study represents a significant advancement for translational research, allowing direct monitoring of biological changes in an organ.
Researchers at the University of Dundee are working on a new effort to tackle tuberculosis, identifying new treatment options for the disease. With a $3 million grant from the Bill & Melinda Gates Foundation, they aim to improve drug discovery and develop new candidate drugs to treat TB.
Lemon, a new framework, enables faster extraction of biological information from large datasets, enhancing drug development. The software can process PDB data in minutes, reducing time from hours to seconds.
The National Cancer Institute's Plated Compound Sets provide a convenient and cost-effective way to screen compounds manually. However, some collections contain pan assay interfering and nonspecific compounds that can generate false hits.
Researchers developed a free online app, eNTRyway, to speed up the discovery of new antibiotics for Gram-negative bacteria. The tool quickly evaluates potential drug compounds and can convert existing drugs into potent killers of Gram-negative pathogens.
TARA Biosystems' human heart-on-a-chip technology shows promise in predicting preclinical systolic and diastolic in vivo observations for novel cardiac drug MYK-491. The platform uses human induced pluripotent stem cells to create physiologically relevant cardiac tissues.
Researchers have identified a new enzyme that inhibits the LRRK2 pathway, which is responsible for most genetic cases of Parkinson's disease. The discovery provides hope for developing new treatments and suggests that the enzyme could benefit all individuals, regardless of whether they have Parkinson's or not.
Researchers at the University of Dundee have discovered an enzyme that could prevent Group A Streptococcus infections by inhibiting a carbohydrate coating on the bacterium's surface. The discovery offers new opportunities for developing antimicrobial drugs with minimal off-target effects.
Chemists at Ohio State University have discovered a new way to synthesize the most common molecule arrangement in medicine, which could make it easier and more efficient for drug makers to produce medicines. This breakthrough could lead to a reduction in waste and an improvement in the production process.
A new drug discovery platform created by Purdue University scientists has the potential to predict the effectiveness of psychoactive drugs in treating mental health illnesses. The platform analyzes compound interactions with proteins and identifies signature patterns for potential therapeutic uses.
The company has proposed a new family of prior distributions: TRIP, which improves Fréchet Inception Distance for GANs and Evidence Lower Bound for VAEs. The model was experimentally validated in cells and animals, demonstrating its potential for accelerating drug discovery.
George Mason University researchers have discovered the exact location where two proteins responsible for hiding cancer cells from the immune system bind. The protein painting technology enables rapid performance testing of drugs, producing results in several days instead of years.
The National Institute on Aging has awarded a grant to establish the Open-AD Drug Discovery Center, which aims to accelerate the development of new drugs for Alzheimer's disease. The center will develop research platforms and openly distribute tools to test the efficacy of Alzheimer's therapies.
The IU-led center will focus on proteins related to the brain's immune system that may contribute to Alzheimer's disease. The goal is to develop high-quality research tools and new technologies needed to broaden the number of targets being investigated for effective medicines.
Researchers are using cloning to recreate esophageal cancer development, identifying potential targets for therapies before the disease becomes fatal. The goal is to develop effective drugs in a fraction of the time it takes Big Pharma.
Researchers developed a method to categorize drugs based on cellular responses, predicting potential clinical effects and reducing side effects. The approach was validated using opioid analgesics, with potential applications in safer opioids and cannabis product testing.
Researchers have developed an AI-powered drug discovery tool that validates novel drug candidates in just 46 days, significantly reducing the traditional 2-3 year timeline. The foundation aims to motivate researchers to harness AI's potential in longevity research and encourage larger developers to adopt AI-powered programs.
A recent study demonstrates the use of Generative Tensorial Reinforcement Learning (GENTRL) to design and validate a novel drug candidate against fibrosis and cancer. The AI process accelerated the development from 46 days, reducing the time estimate by approximately 94%.
Researchers develop algorithm that can predict complex chemical reactions and provide a 'chemical map' to desired products, reducing time in preclinical drug discovery and materials science. The model achieves high accuracy by distilling patterns of reactivity from millions of published chemical reactions.
A new AI system, Generative Tensorial Reinforcement Learning (GENTRL), was used to generate six novel inhibitors of DDR1 kinase target in just 21 days. Four compounds showed activity in biochemical assays, and one lead candidate demonstrated favorable pharmacokinetics in mice.
Patrick M. Woster, Ph.D., has been inducted into the Medicinal Chemistry Hall of Fame for his significant contributions to the field, including the discovery of novel therapeutics such as antimalarial and antibacterial agents. His work also emphasizes the crucial role of medicinal chemists in the drug discovery pipeline.
Quantifying physicochemical properties is crucial for understanding drug interactions and mechanisms. The article highlights the importance of using contemporary methods to improve subpar testing outcomes, enabling better structure-property relationships and accelerated predictive models.
Northwestern University scientists have received a $3.1 million grant to investigate drug therapies for ALS, targeting protein aggregation and upper motor neurons. Promising early results suggest compounds may have broader applications for neurodegeneration.
The study demonstrated the potential of body-on-a-chip systems to transform the drug discovery process by accurately predicting cardiotoxic mechanisms and replicating in vivo results. The findings support the use of in vitro models to evaluate temporal pharmacokinetic/pharmacodynamic relationships.
Researchers at Harvard University have synthesized sufficient quantities of the potent anti-cancer agent E7130 to enable rigorous studies of its biological activity, pharmacological properties, and efficacy. The team's achievement is a landmark in drug discovery, providing new insights into the molecule's complex mode of action and pot...
Researchers at Dartmouth College developed a new strategy for drug discovery and development, enabling the creation of targeted therapies against cancer and neurodegeneration. The technique produces potent compounds selectively toxic to glioblastoma while showing minimal harm to healthy brain cells.
The June issue of SLAS Technology introduces a new sample management collection, enabling the use of disease-relevant cells and tissues in miniaturized biology. This shift reduces drug discovery attrition by mimicking the disease state more effectively.
A leading scientist proposes five rules to tackle antibiotic resistance, including protecting new drugs and using diverse antimicrobials. The World Health Organisation warns of a post-antibiotic era, and the study aims to provide a roadmap for integrated microbial management.
Researchers have developed a method to identify potential targets for new drugs using live blood cells from patients with mental health disorders. The approach has the potential to accelerate drug discovery and personalized medicine for neuropsychiatric disorders, reducing animal testing and improving treatment outcomes.
A new digital filter approach aims to improve chemical measurements, enabling faster drug development and clinical trials. The technology, developed by Purdue University professor Garth Simpson, uses non-negative matrix factorization to analyze large data sets and remove timing artifacts.
Researchers at Tel Aviv University have discovered a homeostatic mechanism that regulates brain activity in neural circuits, paving the way for new treatments of epilepsy. A commonly used medication for multiple sclerosis may also be effective in suppressing seizures in patients with Dravet syndrome.
Researchers from Hong Kong Baptist University have invented a new method that can detect target molecules in pharmaceuticals and pesticides in just five minutes. This breakthrough could lead to the production of higher quality medicinal drugs with no side effects.
Researchers have developed a new chemical synthesis strategy, pharmacophore-directed retrosynthesis (PDR), to harvest information from natural products and identify novel, simpler derivatives. This approach may lead to selective protection of neurons and prevention of immune system rejection in organ transplants.
A new project at the Carl R. Woese Institute for Genomic Biology aims to discover a thousand new ribosomally synthesized and post-translationally modified peptides (RiPPs) using synthetic biology and automation. The project uses an automated robotic system, called iBioFAB, to identify new RiPPs and create new molecules.
DNDi and Atomwise collaborate to develop first-in-class treatments for Chagas disease using AI, delivering drug-like compounds that will undergo further optimization and potential drug development. The research aims to address a public health challenge affecting millions worldwide.
A new drug targeting brain inflammation, MW-151, is ready for its first round of human testing. The medication aims to block 'bad' inflammation that destroys neurons in Alzheimer's disease.
Researchers have discovered parameters governing drug encapsulation, giving more control over the slow release of drugs in patients. This breakthrough enables fewer trial-and-error experiments in drug design, reducing side effects and facilitating personalized therapeutic treatments.
Researchers from Peking University and Insilico Medicine are collaborating on AI-powered drug discovery methods, aiming to accelerate pharmaceutical research and development. The project will focus on various applications of AI in drug discovery, including target identification, compound generation, and personalized medicine.
Virginia Tech professor Webster Santos has received a grant to discover drugs that inhibit a small molecule transporter, which modulates the immune system and could treat multiple sclerosis. The collaboration aims to develop SPNS2 inhibitors for treating MS and metastatic cancers.
Researchers developed 13 bioluminescence sensors to measure intracellular signaling pathways and detect pharmaceutical action. The biosensors provide a complete answer, or 'signaling profile,' allowing for more accurate drug testing and development.
Researchers have developed a strategy to create new small-molecule drugs by reprogramming rapamycin. A library of 45,000 altered molecules was screened to identify a new compound, rapadocin, that inhibits cellular nucleoside uptake without toxicity. Rapadocin targets ENT1 protein and shows promise for treating kidney injury.
Researchers at EPFL developed a photoelectrocatalytic arene C-H amination method, producing pharmaceutical molecules like metaxalone and benzethonium chloride. Hematite semiconductor is used as a catalyst under visible light, offering a low-cost and energy-efficient alternative to traditional methods.
The open-science model is being expanded to neurodegenerative diseases such as Parkinson's and Amyotrophic Lateral Sclerosis. M4ND Pharma will pursue promising new genetic drug targets, sharing progress with the scientific community through regular online meetings.
A machine learning algorithm has been developed to speed up the process of discovering new medicines, identifying four new molecules that activate a protein relevant to symptoms of Alzheimer's disease and schizophrenia. The algorithm is twice as efficient as industry standards and can analyze vast amounts of chemical data.
Scientists at UCSF have developed the world's largest virtual pharmacology platform, enabling them to screen hundreds of millions of never-before-synthesized compounds for potential drugs. The platform has identified remarkably potent antibacterial and psychiatric drug candidates.
Scientists have created an 'unclonable' tag that can never be replicated, using physical unclonable functions (PUFs) with microparticles forming random patterns. The system correctly identified 76% of the tags, with estimated 2.5 x 10^120 unique codes.
Researchers identified a new process capable of generating antibiotic resistance in bacteria up to 600 million years ago. The discovery highlights the need for combined multidrug treatments and reduced agricultural use of antibacterials.
A University of Illinois team has created a synthetic manganese catalyst that can oxidize aliphatic scaffolds in the presence of aromatics, enabling chemists to produce new drugs from old ones. The discovery could expedite the drug discovery process and identify metabolites without requiring new syntheses.
USC scientists have made groundbreaking discoveries about a tick-borne virus, including its molecular mechanisms and potential therapeutic targets. The research could lead to the development of medications and a vaccine to prevent or treat hemorrhagic fever.
Researchers at Purdue University have discovered two peptides that selectively activate beneficial cellular pathways without causing unwanted side effects. These peptides are being investigated in preclinical studies for their ability to regulate dietary intake and have shown promising results.
Kinetoplastid and apicomplexan parasites are targeted by inhibiting thymidylate biosynthesis, leading to decreased dTTP levels and cell death. Novel inhibitors with anti-parasitic activity have been identified in the review.
The article discusses how reperfusion injury amplifies cell damage after tissue perfusion. Calcium handling mechanisms, including Ca2+ ion elevation, predispose patients to mitochondrial failure, hyper-contracture, and proteolysis.
A study by the University of São Paulo's Biomedical Science Institute shows that sofosbuvir can eliminate both chikungunya and yellow fever viruses without damaging human cells. This finding has significant implications for public health, particularly in Brazil where a chikungunya epidemic is forecasted.
A new study assesses the reliability of CETSA HT for early drug discovery, demonstrating good correlations with other assay formats. The technology highlights different compound responses, providing a better understanding of cellular effects.
Researchers found that metformin and syrosingopine combination blocks critical energy production step, driving cancer cells to death. The duo targets NAD+ regeneration, preventing lactate export and leading to cell demise.
Researchers at Weizmann Institute of Science have revealed a previously unknown mechanism underlying anxiety. Targeting this biochemical pathway may help develop new therapies for alleviating the symptoms of anxiety disorders.
Ruiwen Zhang, a University of Houston College of Pharmacy scientist, has been recognized as a leader in cancer prevention and treatment drug discovery and development. He has made major contributions to the discovery of cancer pharmacogenetic/pharmacogenomic syndrome and developed novel gene silencing technologies.
A comprehensive review highlights novel approaches to slow or prevent Alzheimer's disease, focusing on the effects of aging on the brain. Combination therapy is deemed necessary for better treatment outcomes, similar to other major diseases.
A new research developed a platform to predict compounds with clinical potential for treating brain diseases. The platform uses machine learning and whole-brain activity imaging to identify drugs with higher therapeutic and clinical translation potential, accelerating the drug development process.