List view / Grid view
A newly developed AI-driven technique could dramatically speed up the discovery of drugs and advanced materials, enabling scientists to design chemically valid, property-targeted molecules in minutes rather than years.
AI is moving from a supporting role into the core of drug discovery. By 2026, it is expected to shape how targets are chosen, how biology is analysed and how development decisions are made.
Schrödinger has announced a collaboration with Eli Lilly’s TuneLab platform, integrating advanced AI-driven drug discovery workflows into its LiveDesign enterprise informatics system.
Scientists have used advanced computer modelling and lab techniques to design potential new diabetes drugs that improve insulin sensitivity.
Scientists have developed a new mathematical ‘Noise Controller’ that can stabilise random cellular fluctuations, offering a potential breakthrough in preventing cancer recurrence and antibiotic resistance.
The DMTA cycle depends on clear data flow, yet most labs still work across disconnected systems. Sean McGee, Director of Product at Certara, explains how better infrastructure and AI can help teams work faster and make decisions with more confidence.
Researchers at LMU Munich have developed Bits2Bonds, the first platform to fuse molecular simulations with machine learning – accelerating the discovery of polymer carriers for therapeutic RNA.
A new study has revealed that many kinase inhibitors – key drugs used in cancer and other diseases – also trigger the accelerated degradation of their target proteins, which could inform future therapies.
Nonequilibrium switching (NES) offers a faster, more scalable way to predict how strongly drugs bind to their targets. By replacing slow equilibrium simulations with rapid, parallel transitions, NES delivers accurate free energy predictions at speed.
Tasca Therapeutics is using chemical proteomics to map lipid-binding pockets on proteins. By targeting auto-palmitoylation, the company aims to turn previously undruggable cancer drivers into viable therapeutic targets.
A Japanese research team has simulated how RNA molecules fold, using cutting-edge computational tools to model complex structures with accuracy – a breakthrough that could accelerate the development of RNA-based medicines and therapies.
Dr Alan Nafiiev evaluates template-based, docking and template-free approaches to PPI prediction, highlighting how AI can enhance structural accuracy.
Scientists have created a new class of ultra-stable chiral molecules – a discovery that could lead to more precise drug design by preventing potentially harmful molecular “flipping” over time
Researchers at the University of North Carolina at Charlotte have used artificial intelligence to look at how the H5N1 bird flu virus is evolving to evade the immune system - insights that could make way for development of effective future therapies.
Researchers at Southern Medical University have developed a self-propelled ferroptosis nanoinducer that penetrates deeper into tumour tissues - offering a new strategy for safer and more effective cancer treatment.
