Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full …
C Hsu, R Verkuil, J Liu, Z Lin, B Hie… - International …, 2022 - proceedings.mlr.press
We consider the problem of predicting a protein sequence from its backbone atom coordinates. Machine learning approaches to this problem to date have been limited by the …
The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases …
Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can …
JS Lee, J Kim, PM Kim - Nature Computational Science, 2023 - nature.com
The generation of de novo protein structures with predefined functions and properties remains a challenging problem in protein design. Diffusion models, also known as score …
Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the simulation of larger and …
Learning the design patterns of proteins from sequences across evolution may have promise toward generative protein design. However it is unknown whether language …
Advanced Artificial Intelligence (AI) enabled large language models (LLMs) to revolutionize Natural Language Processing (NLP). Their adaptation to protein sequences spawned the …
More than three billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here we show that language models trained on tokens …