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The strongest binder from a primary screen is not necessarily the best drug starting point. Peptide discovery campaigns should capture molecular architecture, biological context, t...
Sponsored content brought to you by Artificial intelligence is dramatically accelerating early drug discovery. Models can screen chemical space, predict structures, optimize proper...
While many AI companies rely exclusively on public datasets and external academic partnerships to train computational models, running an in-house laboratory allows researchers to p...
Using deep learning, researchers predict tautomeric states - an important foundation for reliable drug design.
The articles in this eBook examine key trends driving this transformation, from AI-enabled discovery and experimental validation to advanced cell, gene, and biologic platforms, and...
The system, designed by Stanford researchers, identified which drugs are more likely to succeed in trials and even proposed a cancer treatment a major drugmaker later landed on too...
A team of researchers in China has combined machine learning, molecular docking and molecular dynamics simulations into a single computational pipeline that can sift through tens o...
Researchers trained an AI model to learn chemical patterns associated with stability in drug-like molecules and accurately predict where their hydrogen atoms should be positioned....
Take a look at 13 of the top AI drug discovery companies around today, using their technology to speed up the drug discovery process. The post 13 AI drug discovery companies you sh...
by Wenyu Zhang, Yizheng Wang, Yixiao Zhai, Pinglu Zhang, Yijie Ding, Quan Zou The rapid emergence of drug-resistant pathogens poses a critical threat to global health. With tradit...
A flexible metal-organic framework called APF-40 reveals the 3D structures of large drug molecules from microgram-scale samples, including doramectin.
Researchers have developed fluorescence polarization and thermal shift screening methods to identify small-molecule bait ligands for the bacterial ClpC N-terminal domain, a key ste...
GEN’s virtual summit returns on November 4 to examine how AI is reshaping R&D timelines and real-world patient translation. From big pharma to AI-natives, this year’s program f...
Backed by multiple Sec61 inhibitor families, proteomics and cryo-EM datasets, and machine learning-driven design, Enodia Therapeutics’ platform is the foundation of a new research...
As wet-lab validation becomes increasingly important to support artificial intelligence-enhanced science, research science points out that human-relevant cell models provide an imp...
Unlocking the Arctic’s subterranean DNA must ensure a delicate balance between scientific rigor and ethical stewardship. Synthetic biology and chemistry could help discovery of new...
A year-long collaboration involving scientists at PerturbAI, OpenAI, and their other institutions yielded an AI reasoning framework that turns datasets into testable hypotheses, wi...
Insider Brief A team of Singapore-based researchers reports that a hybrid quantum-classical method could offer a new route to studying drug candidates using today’s Noisy Intermedi...
The KTP provides access to academic expertise while embedding that capability within Centrix’s business. This project represents an opportunity to apply advanced data science techn...
Intrinsically disordered proteins have resisted structure-based drug discovery because they lack a stable fold to model. Talus Bio's structure-free AI model sidesteps that limitati...
In a result that is already sending ripples through the fields of neuroscience and drug design, chemists at the University of California, Davis have performed something akin to a m...
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