- See Also
-
Links
- “Sperm Can’t Unlock an Egg Without This Ancient Molecular Key”
- “Training Compute-Optimal Protein Language Models”, Cheng et al 2024
- “Atomically Accurate de Novo Design of Single-Domain Antibodies”, Bennett et al 2024
- “Press Release: The Nobel Prize in Chemistry 2024”
- “AlphaFold2 Structures Template Ligand Discovery”, Lyu et al 2023
- “Self-Play Reinforcement Learning Guides Protein Engineering”, Wang et al 2023c
- “Evolutionary-Scale Prediction of Atomic Level Protein Structure With a Language Model”, Lin et al 2022
- “OpenFold: Retraining AlphaFold2 Yields New Insights into Its Learning Mechanisms and Capacity for Generalization”, Ahdritz et al 2022
- “Top-Down Design of Protein Nanomaterials With Reinforcement Learning”, Lutz et al 2022
- “Genome-Wide Prediction of Disease Variants With a Deep Protein Language Model”, Brandes et al 2022
- “Accurate Prediction of Transition Metal Ion Location via Deep Learning”, Dürr et al 2022
- “Antibody Optimization Enabled by Artificial Intelligence Predictions of Binding Affinity and Naturalness”, Bachas et al 2022
- “HelixFold-Single: MSA-Free Protein Structure Prediction by Using Protein Language Model As an Alternative”, Fang et al 2022
- “OmegaFold: High-Resolution de Novo Structure Prediction from Primary Sequence”, Wu et al 2022
- “HelixFold: An Efficient Implementation of AlphaFold2 Using PaddlePaddle”, Wang et al 2022
- “Robust Deep Learning Based Protein Sequence Design Using ProteinMPNN”, Dauparas et al 2022
- “State-Of-The-Art Estimation of Protein Model Accuracy Using AlphaFold”, Roney & Ovchinnikov 2022
- “FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours”, Cheng et al 2022
- “AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-Dependent Kinase 20 (CDK20) Small Molecule Inhibitor”, Ren et al 2022
- “The Accuracy of Protein Structures in Solution Determined by AlphaFold and NMR”, Fowler & Williamson 2022
- “Protein Structure Predictions to Atomic Accuracy With AlphaFold”, Jumper & Hassabis 2022
- “Computed Structures of Core Eukaryotic Protein Complexes”, Humphreys et al 2021
- “Towards a Structurally Resolved Human Protein Interaction Network”, Burke et al 2021
- “A Structural Biology Community Assessment of AlphaFold 2 Applications”, Akdel et al 2021
- “Single-Sequence Protein Structure Prediction Using Language Models from Deep Learning”, Chowdhury et al 2021
- “Can AlphaFold2 Predict Protein-Peptide Complex Structures Accurately?”, Ko & Lee 2021
- “Deep Neural Language Modeling Enables Functional Protein Generation across Families”, Madani et al 2021
- “Accurate Prediction of Protein Structures and Interactions Using a 3-Track Network”, Baek et al 2021
- “Deep Learning Methods in Protein Structure Prediction”, Torrisi et al 2020
- “Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model”, Wang et al 2016
- “Predicting Protein Structures With a Multiplayer Online Game”, Cooper et al 2010
- “AlphaFold Protein Structure Database”
- “The Illustrated AlphaFold”
- “Fabian Fuchs”
- “Trainable, Memory-Efficient, and GPU-Friendly PyTorch Reproduction of AlphaFold 2”
- “Open Source Code for AlphaFold”
- “AlphaFold @ CASP13: ‘What Just Happened?’”
- “AlphaFold2 @ CASP14: ‘It Feels like One’s Child Has Left Home.’”
- “The AlphaFold2 Method Paper: A Fount of Good Ideas”
- “Did DeepMind Solve The Protein Folding Problem?”
- “AI Is Ushering In a New Scientific Revolution”
- “CASP14: What Google DeepMind’s AlphaFold 2 Really Achieved, and What It Means for Protein Folding, Biology and Bioinformatics”
- “AlphaFold 2 Is Here: What’s behind the Structure Prediction Miracle”
- “A.I. Predicts the Shapes of Molecules to Come”
- “DeepMind's AlphaFold Changed How Researchers Work”
- “This AI Software Nearly Predicted Omicron’s Tricky Structure”
- Sort By Magic
- Wikipedia
- Miscellaneous
- Bibliography
See Also
Links
“Sperm Can’t Unlock an Egg Without This Ancient Molecular Key”
Sperm Can’t Unlock an Egg Without This Ancient Molecular Key
“Training Compute-Optimal Protein Language Models”, Cheng et al 2024
“Atomically Accurate de Novo Design of Single-Domain Antibodies”, Bennett et al 2024
Atomically accurate de novo design of single-domain antibodies
“Press Release: The Nobel Prize in Chemistry 2024”
“AlphaFold2 Structures Template Ligand Discovery”, Lyu et al 2023
“Self-Play Reinforcement Learning Guides Protein Engineering”, Wang et al 2023c
“Evolutionary-Scale Prediction of Atomic Level Protein Structure With a Language Model”, Lin et al 2022
Evolutionary-scale prediction of atomic level protein structure with a language model
“OpenFold: Retraining AlphaFold2 Yields New Insights into Its Learning Mechanisms and Capacity for Generalization”, Ahdritz et al 2022
“Top-Down Design of Protein Nanomaterials With Reinforcement Learning”, Lutz et al 2022
Top-down design of protein nanomaterials with reinforcement learning
“Genome-Wide Prediction of Disease Variants With a Deep Protein Language Model”, Brandes et al 2022
Genome-wide prediction of disease variants with a deep protein language model
“Accurate Prediction of Transition Metal Ion Location via Deep Learning”, Dürr et al 2022
Accurate prediction of transition metal ion location via deep learning
“Antibody Optimization Enabled by Artificial Intelligence Predictions of Binding Affinity and Naturalness”, Bachas et al 2022
“HelixFold-Single: MSA-Free Protein Structure Prediction by Using Protein Language Model As an Alternative”, Fang et al 2022
“OmegaFold: High-Resolution de Novo Structure Prediction from Primary Sequence”, Wu et al 2022
OmegaFold: High-resolution de novo structure prediction from primary sequence
“HelixFold: An Efficient Implementation of AlphaFold2 Using PaddlePaddle”, Wang et al 2022
HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle
“Robust Deep Learning Based Protein Sequence Design Using ProteinMPNN”, Dauparas et al 2022
Robust deep learning based protein sequence design using ProteinMPNN
“State-Of-The-Art Estimation of Protein Model Accuracy Using AlphaFold”, Roney & Ovchinnikov 2022
State-of-the-Art Estimation of Protein Model Accuracy using AlphaFold
“FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours”, Cheng et al 2022
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours
“AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-Dependent Kinase 20 (CDK20) Small Molecule Inhibitor”, Ren et al 2022
“The Accuracy of Protein Structures in Solution Determined by AlphaFold and NMR”, Fowler & Williamson 2022
The accuracy of protein structures in solution determined by AlphaFold and NMR
“Protein Structure Predictions to Atomic Accuracy With AlphaFold”, Jumper & Hassabis 2022
Protein structure predictions to atomic accuracy with AlphaFold:
“Computed Structures of Core Eukaryotic Protein Complexes”, Humphreys et al 2021
“Towards a Structurally Resolved Human Protein Interaction Network”, Burke et al 2021
Towards a structurally resolved human protein interaction network
“A Structural Biology Community Assessment of AlphaFold 2 Applications”, Akdel et al 2021
A structural biology community assessment of AlphaFold 2 applications
“Single-Sequence Protein Structure Prediction Using Language Models from Deep Learning”, Chowdhury et al 2021
Single-sequence protein structure prediction using language models from deep learning
“Can AlphaFold2 Predict Protein-Peptide Complex Structures Accurately?”, Ko & Lee 2021
Can AlphaFold2 predict protein-peptide complex structures accurately?
“Deep Neural Language Modeling Enables Functional Protein Generation across Families”, Madani et al 2021
Deep neural language modeling enables functional protein generation across families
“Accurate Prediction of Protein Structures and Interactions Using a 3-Track Network”, Baek et al 2021
Accurate prediction of protein structures and interactions using a 3-track network
“Deep Learning Methods in Protein Structure Prediction”, Torrisi et al 2020
“Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model”, Wang et al 2016
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
“Predicting Protein Structures With a Multiplayer Online Game”, Cooper et al 2010
Predicting protein structures with a multiplayer online game
“AlphaFold Protein Structure Database”
“The Illustrated AlphaFold”
“Fabian Fuchs”
“Trainable, Memory-Efficient, and GPU-Friendly PyTorch Reproduction of AlphaFold 2”
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
“Open Source Code for AlphaFold”
“AlphaFold @ CASP13: ‘What Just Happened?’”
“AlphaFold2 @ CASP14: ‘It Feels like One’s Child Has Left Home.’”
AlphaFold2 @ CASP14: ‘It feels like one’s child has left home.’:
“The AlphaFold2 Method Paper: A Fount of Good Ideas”
“Did DeepMind Solve The Protein Folding Problem?”
“AI Is Ushering In a New Scientific Revolution”
“CASP14: What Google DeepMind’s AlphaFold 2 Really Achieved, and What It Means for Protein Folding, Biology and Bioinformatics”
“AlphaFold 2 Is Here: What’s behind the Structure Prediction Miracle”
AlphaFold 2 is here: what’s behind the structure prediction miracle:
“A.I. Predicts the Shapes of Molecules to Come”
“DeepMind's AlphaFold Changed How Researchers Work”
“This AI Software Nearly Predicted Omicron’s Tricky Structure”
This AI Software Nearly Predicted Omicron’s Tricky Structure:
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
protein-design protein-optimization ligand-discovery sequence-modeling deep-learning
protein-folding drug-discovery structure-prediction language-models protein-augmented alpha-folding
protein-prediction
protein-structure
Wikipedia
Miscellaneous
-
https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/: -
https://ccsp.hms.harvard.edu/wp-content/uploads/2020/11/AlphaFold-at-CASP13-AlQuraishi.pdf: -
https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe -
https://www.nytimes.com/2023/01/09/science/artificial-intelligence-proteins.html -
https://www.quantamagazine.org/how-ai-revolutionized-protein-science-but-didnt-end-it-20240626/ -
https://www.quantamagazine.org/new-ai-tools-predict-how-lifes-building-blocks-assemble-20240508/:
Bibliography
-
https://www.biorxiv.org/content/10.1101/2024.06.06.597716.full: “Training Compute-Optimal Protein Language Models”, -
https://www.biorxiv.org/content/10.1101/2023.12.20.572662.full: “AlphaFold2 Structures Template Ligand Discovery”, -
https://www.biorxiv.org/content/10.1101/2022.11.20.517210.full: “OpenFold: Retraining AlphaFold2 Yields New Insights into Its Learning Mechanisms and Capacity for Generalization”, -
https://arxiv.org/abs/2207.13921#baidu: “HelixFold-Single: MSA-Free Protein Structure Prediction by Using Protein Language Model As an Alternative”, -
https://arxiv.org/abs/2207.05477#baidu: “HelixFold: An Efficient Implementation of AlphaFold2 Using PaddlePaddle”, -
https://arxiv.org/abs/2203.00854: “FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours”, -
2021-humphreys.pdf: “Computed Structures of Core Eukaryotic Protein Complexes”,