- See Also
-
Links
- “AlphaFold2 Structures Template Ligand Discovery”, Lyu et al 2023
- “Self-Play Reinforcement Learning Guides Protein Engineering”, Wang et al 2023c
- “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
- “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
- “ESMfold: Language Models of Protein Sequences at the Scale of Evolution Enable Accurate Structure Prediction”, Lin 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
- “Predicting Protein Structures With a Multiplayer Online Game”, Cooper et al 2010
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“AlphaFold2 Structures Template Ligand Discovery”, Lyu et al 2023
“Self-Play Reinforcement Learning Guides Protein Engineering”, Wang et al 2023c
“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
“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
“ESMfold: Language Models of Protein Sequences at the Scale of Evolution Enable Accurate Structure Prediction”, Lin et al 2022
“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
“Predicting Protein Structures With a Multiplayer Online Game”, Cooper et al 2010
Predicting protein structures with a multiplayer online game
Wikipedia
-
Critical Assessment of Techniques for Protein Structure Prediction:
-
Protein structure prediction:
View External Link:
-
Transformer (machine learning model):
View External Link:
https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)
Miscellaneous
-
https://ccsp.hms.harvard.edu/wp-content/uploads/2020/11/AlphaFold-at-CASP13-AlQuraishi.pdf: -
https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/: -
https://moalquraishi.wordpress.com/2021/07/25/the-alphafold2-method-paper-a-fount-of-good-ideas/: -
https://subcriticalappraisal.com/2020/Did-DeepMind-solve-the-protein-folding-problem/: -
https://twitter.com/OriolVinyalsML/status/1612514485201166347: -
https://twitter.com/andrewwhite01/status/1634728559506870274 -
https://twitter.com/maxjaderberg/status/1483098866098524167: -
https://www.deepmind.com/blog/alphafold-reveals-the-structure-of-the-protein-universe -
https://www.nytimes.com/2021/07/22/technology/deepmind-ai-proteins-folding.html -
https://www.nytimes.com/2023/01/09/science/artificial-intelligence-proteins.html -
https://www.technologyreview.com/2022/02/23/1045016/ai-deepmind-demis-hassabis-alphafold/: -
https://www.wired.com/story/ai-software-nearly-predicted-omicrons-tricky-structure/:
Link Bibliography
-
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”, Guoxia Wang, Xiaomin Fang, Zhihua Wu, Yiqun Liu, Yang Xue, Yingfei Xiang, Dianhai Yu, Fan Wang, Yanjun Ma -
https://arxiv.org/abs/2203.00854: “FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours”, Shenggan Cheng, Ruidong Wu, Zhongming Yu, Binrui Li, Xiwen Zhang, Jian Peng, Yang You -
2021-humphreys.pdf: “Computed Structures of Core Eukaryotic Protein Complexes”, -
https://www.biorxiv.org/content/10.1101/2021.08.02.454840.full: “Single-Sequence Protein Structure Prediction Using Language Models from Deep Learning”,