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Protein Structure Prediction
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MegaFold: System-Level Optimizations for Accelerating Protein Structure Prediction Models
Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion
Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data
Aligning Protein Conformation Ensemble Generation with Physical Feedback
Unfolding AlphaFold's Bayesian Roots in Probability Kinematics
Transformers in Protein: A Survey
PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models
LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization
Cognitio Emergens: Agency, Dimensions, and Dynamics in Human-AI Knowledge Co-Creation
AutoLoop: a novel autoregressive deep learning method for protein loop prediction with high accuracy
Exploring zero-shot structure-based protein fitness prediction
From sequence to protein structure and conformational dynamics with AI/ML
AI-guided Antibiotic Discovery Pipeline from Target Selection to Compound Identification
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks
Advanced Deep Learning Methods for Protein Structure Prediction and Design
Towards Interpretable Protein Structure Prediction with Sparse Autoencoders
Non-Canonical Crosslinks Confound Evolutionary Protein Structure Models
Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold2
A Model-Centric Review of Deep Learning for Protein Design
Protein Large Language Models: A Comprehensive Survey
MotifBench: A standardized protein design benchmark for motif-scaffolding problems
Deep Learning of Proteins with Local and Global Regions of Disorder
PyMOLfold: Interactive Protein and Ligand Structure Prediction in PyMOL
CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios
Human Genome Book: Words, Sentences and Paragraphs
Computational Protein Science in the Era of Large Language Models (LLMs)
Generative diffusion model with inverse renormalization group flows
A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design
Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning
Applications of Knot Theory for the Improvement of the AlphaFold Protein Database
Training on test proteins improves fitness, structure, and function prediction
Ratio law: mathematical descriptions for a universal relationship between AI performance and input samples
Beyond Current Boundaries: Integrating Deep Learning and AlphaFold for Enhanced Protein Structure Prediction from Low-Resolution Cryo-EM Maps
FoldMark: Protecting Protein Generative Models with Watermarking
CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.