Computational scientists and neutron structural biologists from Oak Ridge National Laboratory developed an integrated workflow using small-angle neutron scattering (SANS), atomistic molecular dynamics (MD) simulation, and an autoencoder-based deep learning (DL) algorithm to characterize structural ensembles of intrinsically disordered proteins (IDPs). IDPs make up 33% to 50% of eukaryotic proteomes and are involved in critical cellular function and regulation.
Oak Ridge National Laboratory (ORNL) researchers have developed a new strategy for molecule generation to accelerate molecular design for drug discovery applications.
Generative machine learning models, including GANs (Generative Adversarial Networks), are a powerful tool toward searching chemical space for desired functionalities.
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.
As home to three top-ranked supercomputers of the last decade, the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) has become synonymous with scientific computing at the largest scales. Getting the most out of these science machines, however, requires a w...
It’s not enough to design new drugs. For drugs to be effective, they have to be delivered safely and intact to affected areas of the body. And drug delivery, much like drug design, is an immensely complex task.
