I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction
Most proteins in cells are composed of multiple folding units (or domains) to perform
complex functions in a cooperative manner. Relative to the rapid progress in single-domain …
complex functions in a cooperative manner. Relative to the rapid progress in single-domain …
Transformer-based deep learning for predicting protein properties in the life sciences
Recent developments in deep learning, coupled with an increasing number of sequenced
proteins, have led to a breakthrough in life science applications, in particular in protein …
proteins, have led to a breakthrough in life science applications, in particular in protein …
A method for multiple-sequence-alignment-free protein structure prediction using a protein language model
Protein structure prediction pipelines based on artificial intelligence, such as AlphaFold2,
have achieved near-experimental accuracy. These advanced pipelines mainly rely on …
have achieved near-experimental accuracy. These advanced pipelines mainly rely on …
Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
While AlphaFold2 can predict accurate protein structures from the primary sequence,
challenges remain for proteins that undergo conformational changes or for which few …
challenges remain for proteins that undergo conformational changes or for which few …
trRosettaRNA: automated prediction of RNA 3D structure with transformer network
RNA 3D structure prediction is a long-standing challenge. Inspired by the recent
breakthrough in protein structure prediction, we developed trRosettaRNA, an automated …
breakthrough in protein structure prediction, we developed trRosettaRNA, an automated …
When will RNA get its AlphaFold moment?
B Schneider, BA Sweeney, A Bateman… - Nucleic Acids …, 2023 - academic.oup.com
The protein structure prediction problem has been solved for many types of proteins by
AlphaFold. Recently, there has been considerable excitement to build off the success of …
AlphaFold. Recently, there has been considerable excitement to build off the success of …
Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model
We report two generative deep-learning models that predict amino acid sequences and 3D
protein structures on the basis of secondary-structure design objectives via either the overall …
protein structures on the basis of secondary-structure design objectives via either the overall …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problems
MJ Buehler - Journal of the Mechanics and Physics of Solids, 2023 - Elsevier
We report a flexible multi-modal mechanics language model, MeLM, applied to solve
various nonlinear forward and inverse problems, that can deal with a set of instructions …
various nonlinear forward and inverse problems, that can deal with a set of instructions …
[HTML][HTML] Deep language models for interpretative and predictive materials science
Y Hu, MJ Buehler - APL Machine Learning, 2023 - pubs.aip.org
Machine learning (ML) has emerged as an indispensable methodology to describe,
discover, and predict complex physical phenomena that efficiently help us learn underlying …
discover, and predict complex physical phenomena that efficiently help us learn underlying …