Latest Research in Ligand Binding Energetics

Top Research Topics in Ligand Binding Energetics

• Computational Drug Discovery Methods — 8 papers
• Protein Structure and Dynamics — 4 papers
• Quantitative Methods — 3 papers
• Machine Learning in Bioinformatics — 2 papers
• Artificial Intelligence — 2 papers
• Soft Condensed Matter — 2 papers
• Machine Learning — 2 papers
• Carbohydrate Chemistry and Synthesis — 1 papers
• Chemical Synthesis and Analysis — 1 papers
• Biomolecules — 1 papers

Highest-Cited Papers

1. Ligand binding free energy landscapes at the tubulin colchicine site from coarse-grained metadynamics (1 citations)
2. Challenges and Recent Advances in O ‐Sulfation
3. Explainable Artificial Intelligence to Unveil Patterns of Antioxidant Peptides for Free Radical Regulation
4. TorchCraft: Unified binder design by inverting an all-atom structure predictor
5. G-screen: scalable protein-aware virtual screening through flexible ligand alignment
6. STELLAR: A Fragment-Based Docking Workflow for Highly Flexible Long-Chain Biopolymers
7. Conformational landscape of a macrocycle from REST enhanced sampling
8. SaltyMeta: a curated benchmark and protein language model-informed web tool for salty peptide prediction
9. MIRAGE: Measuring Interpolation and Redundancy in Affinity GEneralization
10. Predicting the Post-translational Modification Effects on Protein−Ligand Interactions via End-Point Binding Free Energy Calculation: Database Creation and Strategy Optimization
11. Alchemical Free Energy Perturbation Predicts Relative Binding Affinities of Propofol Analogs and Etomidate Stereoisomers at the GABAAR
12. Conformational Preference Classification of Integrin-Binding Ligands Using Free Energy Perturbation
13. MI-PEFT: Mixture-of-Experts Integrated Parameter-Efficient Fine-Tuning Protein Language Models Improves Acidophilic Proteins Classification
14. Partner-aware Peptide-Protein Interaction Prediction and Target-conditioned Peptide Generation
15. Deep Learning in Enzyme Function Prediction and Novel Enzyme Discovery
16. Persistent Local Laplacian Prediction of Protein–Ligand Binding Affinities
17. Stable simulations do not guarantee functional engagement: a case study of off-target prediction for Seladelpar and Zanamivir
18. A curated benchmark for cofolding models on kinase conformational states
19. Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling
20. Protein language model-generated enzyme sequences exhibit high stability in molecular dynamics simulations

Sub-Regions

• Machine Learning in Bioinformatics — 11 papers
• Computational Drug Discovery Methods — 10 papers
• Carbohydrate Chemistry and Synthesis — 9 papers
• Protein Structure and Dynamics — 8 papers
• Genomics and Rare Diseases — 5 papers
• Machine Learning — 2 papers