Our researchers are developing an automated pipeline that can more accurately identify promising compounds targeting multi-site proteins
Atomwise scientists are improving predictive models for ADMET profiling by including a pre-training step that builds on diverse publicly available datasets
Until recently, a diagnosis of sickle cell disease was considered a death sentence. Now, people with this blood disorder are more likely to live well into adulthood, but they still suffer from severe bouts of pain and an increased risk of infection.
With the programmers on the Atomwise team, we have a steady stream of new ideas for how to improve the AtomNet® platform we created to virtually screen protein targets against billions of compounds to find promising drug candidates.
Atomwise's AIMS Award program supports the under-studied WDR proteins to fuel future drug discovery efforts Back in 2004, some of the world’s leading protein scientists and structural biologists teamed up to form a public-private partnership focused […]
When you’re developing deep learning models, it’s not always obvious why a model performs the way it does. After all, the whole point of deep learning is to let the network teach itself from training data — and the way it assimilates that […]
At the American Chemical Society Fall 2021 National Meeting & Expo, Atomwise members were selected to present their research and work. Learn what our Atoms have been working on below and visit Atomwise at ACS Fall 2021 National Meeting for other […]
At the American Chemical Society Fall 2021 National Meeting & Expo, Atomwise members were selected to present their research and work. Learn what our Atoms have been working on below and visit Atomwise at ACS Fall 2021 National Meeting for other […]
At the American Chemical Society Fall 2021 National Meeting & Expo, Atomwise members were selected to present their research and work. Learn what our Atoms have been working on below and visit Atomwise at ACS Fall 2021 National Meeting for other […]