AlphaFold 3 is a Fantastic Breakthrough and Deserves all the
WEBAlphaFold is helping researchers build models, but it can’t yet simply find all protein states. F@H has always focused on molecular dynamics, while Rosetta@Home (a BOINC project) looked for protein structures and has been more supplanted by AlphaFold. F@H also open sources all its results and was probably used to train AlphaFold.
Announcing AlphaFold 3: our state-of-the-art AI model for
WEBMay 8, 2024 · Yes, AlphaFold has been involved in several recent medical breakthroughs and advancements: Accelerating drug discovery for neglected diseases: The Drugs for Neglected Diseases Initiative (DNDi) is using AlphaFold to help identify new drug candidates for diseases like Chagas disease and leishmaniasis that disproportionately …
alphafold - Reddit
WEBJan 29, 2022 · Secondary Structure confidence on Alphafold. I have used Alphafold to determine the structures for a protein of my interest. While the confidence score is low for the over all prediction, I am curious to know if the secondary structures are accurate. I don’t have much concern about the exact folding of the protein but am concerned if each ...
Best AlphaFold Posts - Reddit
WEBSeems like DeepMind just caused the ImageNet moment for protein folding. Blog post isn't that deeply informative yet (paper is promised to appear soonish). Seems like the improvement over the first version of AlphaFold is mostly usage of transformer/attention mechanisms applied to residue space and com
How important of a breakthrough is DeepMind's AlphaFold?
WEBNov 28, 2021 · Alicecomma. • 3 yr. ago. The problem with AlphaFold is it's trained on a limited set of known data, specifically annotated data where a large number of similar structures exist. It sucks at predicting membrane proteins and some barrel proteins I fed it, as the active sites tend to be cocked up to the point where it can't fit known substrates.
People are overestimating Alphafold and it’s a problem
WEBAlphaFold isn’t saying that, that it is you interpreting the AlphaFold output. Very low pLDDTs are very strongly correlated with flexible or disordered regions. You can’t get high resolution structural info on highly flexible or disordered regions with crystallography or other techniques, so your interpretation would be wrong anyway.
[R] AlphaFold 2 : r/MachineLearning - Reddit
WEBNov 30, 2020 · ADMIN MOD. [R] AlphaFold 2. Research. Seems like DeepMind just caused the ImageNet moment for protein folding. Blog post isn't that deeply informative yet (paper is promised to appear soonish). Seems like the improvement over the first version of AlphaFold is mostly usage of transformer/attention mechanisms applied to residue …
Running AlphaFold on a standalone PC? : r/bioinformatics - Reddit
WEBAug 16, 2023 · I got some feedback on the AlphaFold GitHub page. Insufficient GPU RAM is apparently a frequent cause of crashes. One user said they were able to run 600-residue predictions on a GPU with 8 GB of on-board RAM. Alas, my GTX 1660 has but 6 GB, but if everything scales linearly I could expect to run 450-residue predictions.
Google DeepMind: AlphaFold 3 predicts the structure and ... - Reddit
WEBIn a paper published in Nature, we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important ...
Google DeepMind’s AlphaFold 3 AI for Protein Structure Can
WEBMay 8, 2024 · AlphaFold 3 is theoretically able to predict a protein structure with any ligand, but the issue lies in the current implementation of the web server. Currently, non-commercial users are only limited to a couple dozen “common” ligands, with no way to input a custom SMILES string.