To developing DIProT, the researchers integrated deep learning models with human expertise directly into the design process, thereby enhancing the efficiency and effectiveness of protein design.

Now, Purdue University researchers have designed a novel approach to use deep learning to better understand how proteins interact in the body – paving the way to producing accurate structure models of ...

A major scientific advance in protein modeling developed by Microsoft Research AI for Science, has been published in Science.

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin has ...

Recently, a research team has collaborated to propose a deep learning model for predicting protein conformational changes. This achievement has been published online in Advanced Science.

Technology application and future direction: Deep learning improves protein structure prediction and provides new possibilities for drug design, antibody development, and synthetic biology.

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.

Meanwhile, deep learning models face challenges in modeling complex molecular-protein interactions and capturing sequence-structure dependencies.

TYSONS CORNER, VA, Oct. 17, 2017 ” Amazon Web Services (Nasdaq: AMZN) and Microsoft (Nasdaq: MSFT) have collaborated to launch a deep learning library that will work to support the development ...

The work builds on advances in protein design with deep learning, including the ability to predict a protein structure from a sequence and vice versa, especially as computing power has increased.

Scientists have developed DIProT, an innovative, user-friendly toolkit for protein design. The toolkit utilizes a non-autoregressive deep generative model to address the protein inverse folding ...