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 […]
"Atomwise is a member of NVIDIA Inception, a program that supports AI startups with go-to-market support, expertise, and technology. Atomwise developed the AtomNet® model, the first convolutional neural network (CNN) for drug discovery, capable of […]
At the American Chemical Society Fall 2020 Virtual Meeting & Expo, several Atomwise members and partners were selected to present their research and work. Learn what our Atoms have been working on below and visit Atomwise at ACS Fall 2020 Virtual […]
- by Abraham Heifets, co-founder and CEO of Atomwise originally posted on Medium In their cogent and insightful discussion about the different perspectives of Tech and Biotech investors, Drs. Shaywitz and Gibson make the implicit assumption that AI […]
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