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 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 ...

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 ...

Understanding how molecules interact is central to biology: from decoding how living organisms function to uncovering disease ...

In the rapidly advancing field of computational biology, a review explores the transformative role of deep learning ... structure prediction. Multimodal prediction: The latest AlphaFold 3 model ...

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 ...

This function can tune and adjust itself, over and over at unimaginable levels of complexity, in order to “learn” precisely how a protein sequence mathematically relates to its structure. AlQuraishi ...

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 ...