Atomwise scientists are using machine learning to help University of Alberta researchers find compounds that have the potential to regrow damaged nerves. Nerve damage is a common problem following injury in neurological disorders, but regrowing […]
Our scientists developed AtomNet® PoseRanker to improve our technology’s ability to rank poses for protein-ligand interactions with potential drug compounds Identifying the highest-quality protein-ligand poses generated from structure-based virtual […]
Our scientists have developed a set of chemical benchmarks to better understand exactly what our computational models are learning. At Atomwise, AtomNet® structure-based models can quickly identify active compounds for protein targets from large […]
Atomwise scientists are adapting machine learning models to make predictions about a class of drug compounds that has been challenging to study.
Our researchers are developing an automated pipeline that can more accurately identify promising compounds targeting multi-site proteins
With the implementation of genomic technologies, medicine is becoming increasingly personalized thanks to new treatments that target important molecular targets. In the context of Parkinson's disease, there is an urgent need to identify molecular […]
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