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Unleashing the Power of Big Data: High Content Screening in the...
The demand for high content screening is projected to surge with a projected CAGR of 5.8%
FACE TO FACE: Dr Kanury V S Rao, Co-Founder & CSO,...
In a highly informative conversation, Dr Kanury V S Rao, Co-Founder & Chief Scientific Officer, PredOmix spoke to Rahul Koul, Chief Editor, BioVoice News...
PhoreMost and ThinkCyte collaborate to advance AI based drug discovery
PhoreMost’s SITESEEKER platform, based on the Company’s core proprietary ‘Protein Interference’ (PROTEINi) technology, probes the entire proteome to systematically unmask new and unanticipated druggable sites
No side effects of drugs dropped from NLEM in 2022: Experts
The reasons behind eliminating 26 drugs from the list are not side-effects, but others like the parameters of cost-effectiveness and availability of better drugs, etc., as a government official has revealed so
384 drugs included in NLEM 2022; 34 new drugs added
Dr Mansukh Mandaviya launches National Lists of Essential Medicines (NLEM) 2022
IIT Mandi researchers use natural polymer based smart nanoparticles to treat...
Researchers have developed biodegradable nanoparticles from renewable resources which can release both hydrophilic and hydrophobic drugs having different anticancer mechanisms, thus reducing the dependency on petroleum-based polymers
Plastic vials market to surpass US$ 1.9 billion, applications on rise...
Based on end-use, pharmaceuticals & healthcare industry is anticipated to hold around 81% of the market share by the end of 2032
HempStreet partners Israel’s Gynica to develop phytopharma products
The partnership is aimed to bolster the combined R&D efforts to improve women’s health with cannabinoid-based phytomedicine
Deep learning model to predict adverse drug to drug interactions
Using gene expression data, the new model can predict how some drug-drug interactions can lead to adverse effects in the human body
GIST researchers identify new medicines using interpretable deep learning predictions
New drugs are predicted for target proteins using a new, interpretable deep learning-based model with improved prediction and transparency