Kihara and his team developed a system called DOVE, DOcking decoy selection with Voxel-based deep neural nEtwork, which applies deep learning principles to virtual models of protein interactions. DOVE ...

Although artificial neural networks are powerful classifiers, their internal structures are hard to ... Interpretability of deep learning models: a survey of results. Paper presented at IEEE ...

in order to "learn" precisely how a protein sequence mathematically relates to its structure. AlQuraishi developed a deep-learning model, termed a recurrent geometric network, which focuses on key ...

In the rapidly advancing field of computational biology, a newly peer-reviewed review explores the transformative role of deep learning techniques in revolutionizing protein structure prediction ...

This deep learning-based method utilizes an RNA language model to accurately predict RNA 3D structures. This method addresses the challenges of RNA's intrinsic structural flexibility and the ...

Researchers have developed a deep ... model that takes inspiration from Boltzmann generators, which are highly advanced physics-based machine learning models. PepFlow can also model peptide ...

In a perspectives paper published in Science last week, Ullman argued that neuroscience still has a lot to offer deep learning—and combining the AI darling with brain-like innate structures could ...

AlphaFold, software developed by Google’s DeepMind AI unit to predict the 3D structure of proteins, has received a significant upgrade. It can now model ... whether deep learning, the technique ...