| scholarly article | Q13442814 |
| P356 | DOI | 10.1002/MINF.201600048 |
| P698 | PubMed publication ID | 27870243 |
| P2093 | author name string | Christoph A Sotriffer | |
| Lukas P Pason | |||
| P2860 | cites work | Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. | Q37781709 |
| Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity? | Q38526679 | ||
| Development, validation, and application of adapted PEOE charges to estimate pKa values of functional groups in protein-ligand complexes | Q40295007 | ||
| Accurate Binding Free Energy Predictions in Fragment Optimization | Q40441661 | ||
| Assessment of programs for ligand binding affinity prediction | Q43105884 | ||
| SFCscore(RF): a random forest-based scoring function for improved affinity prediction of protein-ligand complexes | Q45805013 | ||
| Comparative assessment of scoring functions on a diverse test set. | Q48026800 | ||
| SFCscore: scoring functions for affinity prediction of protein-ligand complexes. | Q50131043 | ||
| Classification of current scoring functions. | Q50603070 | ||
| Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. | Q53678858 | ||
| Nearest neighbor pattern classification | Q56219830 | ||
| Can we use docking and scoring for hit-to-lead optimization? | Q80467476 | ||
| Binding MOAD (Mother Of All Databases) | Q81878003 | ||
| Correction to CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes | Q93537055 | ||
| ??? | Q62849501 | ||
| Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening | Q26750642 | ||
| pH-dependent binding modes observed in trypsin crystals: lessons for structure-based drug design | Q27638635 | ||
| Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors | Q28219491 | ||
| Docking ligands into flexible and solvated macromolecules. 4. Are popular scoring functions accurate for this class of proteins? | Q28245128 | ||
| A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking | Q28276262 | ||
| Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs | Q28285760 | ||
| A machine learning-based method to improve docking scoring functions and its application to drug repurposing | Q28304560 | ||
| Application and limitations of X-ray crystallographic data in structure-based ligand and drug design | Q30805804 | ||
| Directed evolution of antibody fragments with monovalent femtomolar antigen-binding affinity | Q30923970 | ||
| Binding affinity prediction with property-encoded shape distribution signatures | Q33757755 | ||
| The statistical-thermodynamic basis for computation of binding affinities: a critical review | Q33915676 | ||
| NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes | Q34240465 | ||
| The experimental uncertainty of heterogeneous public K(i) data. | Q34285419 | ||
| The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures | Q34323005 | ||
| The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure | Q34326042 | ||
| Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results | Q34414010 | ||
| Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set. | Q34414478 | ||
| The PDBbind database: methodologies and updates | Q34424778 | ||
| Further development and validation of empirical scoring functions for structure-based binding affinity prediction | Q34526566 | ||
| CSAR benchmark exercise of 2010: selection of the protein-ligand complexes. | Q35232599 | ||
| Beware of machine learning-based scoring functions-on the danger of developing black boxes | Q35245699 | ||
| CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. | Q35263970 | ||
| Modeling Protein-Ligand Binding by Mining Minima | Q35987132 | ||
| Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. | Q37014096 | ||
| Targeted scoring functions for virtual screening | Q37512717 | ||
| P433 | issue | 11-12 | |
| P921 | main subject | protein function prediction | Q7251473 |
| P304 | page(s) | 541-548 | |
| P577 | publication date | 2016-07-08 | |
| P1433 | published in | Molecular Informatics | Q3319476 |
| P1476 | title | Empirical Scoring Functions for Affinity Prediction of Protein-ligand Complexes | |
| P478 | volume | 35 |
| Q57492456 | Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges |
| Q90416653 | Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method |
| Q90193662 | Key Topics in Molecular Docking for Drug Design |
| Q115741632 | New machine learning and physics-based scoring functions for drug discovery |
| Q91289731 | Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions |
| Q61999145 | Rescoring of docking poses under Occam’s Razor: are there simpler solutions? |
| Q90428394 | Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace |
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