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A novel approach to protein chemical shift prediction from sequences using a protein language model

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Accurate prediction of 11B NMR chemical shift of BODIPYs via machine learning

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A platform of ADAPTive scaffolds: development of CDR-H3 β-hairpin mimics into covalent inhibitors of the PD1/PDL1 immune checkpoint

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Regression Machine Learning Models Used to Predict DFT-Computed NMR Parameters of Zeolites

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Protein C-GeM: A Coarse-Grained Electron Model for Fast and Accurate Protein Electrostatics Prediction

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Expression and purification of 15N-labeled Fra a 1, a strawberry allergen, to prepare samples for NMR measurements

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Aggregation-Prone Structural Ensembles of Transthyretin Collected With Regression Analysis for NMR Chemical Shift

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Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states

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Ab Initio Machine Learning in Chemical Compound Space

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Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data

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Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments

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Machine learning in NMR spectroscopy

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Umbrella Refinement of Ensembles—An Alternative View of Ensemble Optimization

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The conformational landscape of fold-switcher KaiB is tuned to the circadian rhythm timescale

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EFG‐CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models

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Machine learning accelerates quantum mechanics predictions of molecular crystals

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Exploring the Frontiers of Computational NMR: Methods, Applications, and Challenges

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Improved Prediction of Carbonless NMR Spectra by the Machine Learning of Theoretical and Fragment Descriptors for Environmental Mixture Analysis

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Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning

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MIM-ML: A Novel Quantum Chemical Fragment-Based Random Forest Model for Accurate Prediction of NMR Chemical Shifts of Nucleic Acids

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Biomolecular NMR in the AI-assisted structural biology era: Old tricks and new opportunities

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On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins

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Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks

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Targeting protein disorder: the next hurdle in drug discovery

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Analysis of solid-state NMR data facilitated by MagRO_NMRViewJ with Graph_Robot: Application for membrane protein and amyloid.

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Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data

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Rapid Quantification of Protein Secondary Structure Composition from a Single Unassigned 1D 13C Nuclear Magnetic Resonance Spectrum

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UCBShift 2.0: Bridging the Gap from Backbone to Side Chain Protein Chemical Shift Prediction for Protein Structures

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Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution

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SpecDB: A relational database for archiving biomolecular NMR spectral data

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A Practical Guide to All-Atom and Coarse-Grained Molecular Dynamics Simulations Using Amber and Gromacs: A Case Study of Disulfide-Bond Impact on the Intrinsically Disordered Amyloid Beta

Pamela Smardz, Midhun Mohan Anila, Paweł Rogowski, Mai Suan Li, Bartosz Różycki and Pawel Krupa

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Accurate Structure Prediction for Protein Loops Based on Molecular Dynamics Simulations with RSFF2C

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Biomolecular NMR spectroscopy in the era of artificial intelligence

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LEGOLAS: A Machine Learning Method for Rapid and Accurate Predictions of Protein NMR Chemical Shifts

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