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- PROTEIN SECONDARY STRUCTURE PREDICTION USING DEEP CONVOLUTIONAL NEURAL FIELDS Sheng Wang*,1,2, Jian Peng3, Jianzhu Ma1, and Jinbo Xu*,1
- Machine Learning Approaches for Quality Assessment of Protein Structures
- Voromqa: Assessment of Protein Structure Quality Using Interatomic Contact Areas Kliment Olechnovicˇ1,2 and Ceslovas Venclovas1*
- Energy-Based Models for Atomic-Resolution Protein Conformations
- Approach of MODELLER for Protein Homology Modeling
- 1 Tfold-TR: Combining Deep Learning Enhanced Hybrid Potential Energy
- Statistical Computation for Problems in Dynamic Systems and Protein Folding
- In Silico Structure Modeling and Characterization of Hypothetical
- Distance-Based Protein Folding Powered by Deep Learning
- Bayesian Statistical Approach for Protein Residue-Residue Contact Prediction
- A Position-Specific Statistical Potential for Protein Structure and Functional
- Accurate Prediction of Protein Torsion Angles Using
- A Deep-Learning Approach to Contact-Driven Protein
- De Novo Protein Folding Using Statistical Potentials from Deep Learning Group 043 / A7D / Alphafold
- 1 Macromolecular Modeling and Design in Rosetta
- An Introduction to Biomolecular Simulations and Docking
- An Analysis and Evaluation of the Wefold Collaborative for Protein
- Protein Structure Prediction from Primary Sequence
- Statistical and Machine Learning Approaches to Predicting Protein-Ligand Interactions
- Distance-Based Protein Folding Powered by Deep Learning Jinbo Xu Toyota Technological Institute at Chicago 6045 S Kenwood, IL, 60637, USA [email protected]
- CASP13 Abstracts.Pdf
- An Overview of Comparative Modelling and Resources Dedicated to Large-Scale Modelling of Genome Sequences ISSN 2059-7983
- Optimized Atomic Statistical Potentials
- Distance-Based Protein Folding Powered by Deep Learning Jinbo Xu Toyota Technological Institute at Chicago 6045 S Kenwood, IL, 60637, USA [email protected]
- Protein Structure Prediction Rachel
- Knowledge-Based Approaches for Understanding Structure-Dynamics-Function Relationship in Proteins Kannan Sankar Iowa State University
- Protein Structure Prediction
- Synthqa-Hierarchical Machine Learning-Based Protein Quality Assessment
- Advances in Protein Structure Prediction and De Novo Protein Design: a Review
- Alphafold at CASP13 Mohammed Alquraishi 1,2,*
- Protein Model Accuracy Estimation Empowered by Deep Learning And
- The Dependence of All-Atom Statistical Potentials on Structural Training Database
- CASP14 Abstract Book
- A Position-Specific Statistical Potential for Protein Structure and Functional Study
- The Dependence of All-Atom Statistical Potentials on Structural Training Database
- Bayesian Weighting of Statistical Potentials in NMR Structure Calculation
- Bayesian Parameter Estimation in Ising and Potts Models: a Comparative Study with Applications to Protein Modeling