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
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.
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 […]
If you think of virtual drug screening as a video game, with players lining up models of compounds and protein targets to see if they fit together, then binding affinity might be how the game is scored. In drug discovery, we look for strong binding […]
The Atomwise approach to drug discovery is based on the strong belief that virtual screening enabled by sophisticated AI-based computational models will accelerate the development of new drug candidates, and eventually achieve our mission of […]
Here at Atomwise, we are really proud of the AtomNet® tool we’ve built to perform virtual screening of targets against billions of compounds to find promising candidates. This approach has already provided new leads for academic labs and drug […]
You probably hear about AI on a regular basis, but if you’re not a programmer steeped in machine learning algorithms, AI can seem like a mysterious black box.
Drug discovery startup Atomwise, which joined the NVIDIA Inception virtual accelerator program in 2018, developed AtomNet, a convolutional neural network for small molecule drug discovery. The AtomNet AI technology can screen more than 16 billion […]
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