Protein Folding
38 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Protein Folding
PS4
Most implemented papers
Disentangled Dynamic Graph Deep Generation
Extending existing deep generative models from static to dynamic graphs is a challenging task, which requires to handle the factorization of static and dynamic characteristics as well as mutual interactions among node and edge patterns.
QFold: Quantum Walks and Deep Learning to Solve Protein Folding
roberCO/QFold
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Predicting the 3D structure of proteins is one of the most important problems in current biochemical research.
Inferring temporal dynamics from cross-sectional data using Langevin dynamics
Our method is a 'baseline' method which initiates the development of computational models which can be iteratively enhanced through the inclusion of expert knowledge.
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant challenges.
Efficient Architecture Search for Diverse Tasks
While neural architecture search (NAS) has enabled automated machine learning (AutoML) for well-researched areas, its application to tasks beyond computer vision is still under-explored.
Robust deep learning based protein sequence design using ProteinMPNN
dauparas/ProteinMPNN
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bioRxiv 2022
While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta.
State-specific protein-ligand complex structure prediction with a multi-scale deep generative model
The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life.
AlphaFold Distillation for Protein Design
ibm/afdistill
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This model can then be used as a structure consistency regularizer in training the inverse folding model.
Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
compsoftmatterbiophysics-cityu-hk/applying-drl-to-hp-model-for-protein-structure-prediction
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This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice.
