Protein Structure Prediction
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Benchmarks
Most implemented
Highly accurate protein structure prediction with AlphaFold
SE(3) diffusion model with application to protein backbone generation
Distribution-Free, Risk-Controlling Prediction Sets
SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning
Papers
Does Learning Protein Folding Generalize to Broader Reasoning?
Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natural testbed, because one solved structure yields th…
Protein Structure PredictionProtein Structure Prediction: From Evolutionary Constraints to Generative Modeling
Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation. Recent advances in deep learning have…
Protein Structure PredictionMultiple Sequence AlignmentProtein DesignAtom-level Protein Representation Learning Improves Protein Structure Prediction
Recent advances in generative modeling show that pretrained representations can improve generation as conditioning features or alignment targets. Motivated by this, we study protein representations for predicting structu…
Protein Structure PredictionRepresentation LearningDCFold: Efficient Protein Structure Generation with Single Forward Pass
AlphaFold3 introduces a diffusion-based architecture that elevates protein structure prediction to all-atom resolution with improved accuracy. This state-of-the-art performance has established AlphaFold3 as a foundation …
Protein Structure PredictionProtein DesignThe Biosecurity Blind Spot: Systematic Dual-use Detection in Open Science Infrastructure
AI is transforming life sciences research at unprecedented speed, accelerating discovery across protein structure prediction, genome modeling, and drug development (Jumper et al., 2021; Mak et al., 2024). Yet this rapid …
Protein Structure PredictionSampling at intermediate temperatures is optimal for training large language models in protein structure prediction
We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamics to characterize …
Protein Structure Prediction