| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/jcisd/FanSIDSS11 |
| P356 | DOI | 10.1021/CI200377U |
| P932 | PMC publication ID | 3246566 |
| P698 | PubMed publication ID | 22014038 |
| P5875 | ResearchGate publication ID | 51733964 |
| P50 | author | Andrej Šali | Q4756078 |
| John J Irwin | Q38372872 | ||
| Brian K. Shoichet | Q38373802 | ||
| Dina Schneidman-Duhovny | Q47155716 | ||
| P2093 | author name string | Hao Fan | |
| Guangqiang Dong | |||
| P2860 | cites work | Local propensities and statistical potentials of backbone dihedral angles in proteins | Q80489346 |
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| Recognizing native folds by the arrangement of hydrophobic and polar residues | Q71751636 | ||
| Factors affecting the ability of energy functions to discriminate correct from incorrect folds | Q73221953 | ||
| Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence | Q73702777 | ||
| An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function | Q80287324 | ||
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| Evidence for transition-state stabilization by serine-148 in the catalytic mechanism of chloramphenicol acetyltransferase | Q27666027 | ||
| Prediction of new serine proteinase inhibitors | Q27729803 | ||
| Highly selective mechanism-based thrombin inhibitors: structures of thrombin and trypsin inhibited with rigid peptidyl aldehydes | Q27765257 | ||
| Comparative protein modelling by satisfaction of spatial restraints | Q27860866 | ||
| Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors | Q28219491 | ||
| Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy | Q28251042 | ||
| Automated docking screens: a feasibility study | Q28256939 | ||
| Scoring functions for protein-ligand docking | Q28271137 | ||
| Energy functions that discriminate X-ray and near native folds from well-constructed decoys | Q28278562 | ||
| The Cambridge Structural Database: a quarter of a million crystal structures and rising | Q29039683 | ||
| Statistical potential for assessment and prediction of protein structures | Q29615145 | ||
| Verification of protein structures: patterns of nonbonded atomic interactions | Q29615613 | ||
| Modeling of loops in protein structures | Q29615861 | ||
| Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions | Q29616398 | ||
| A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6 | Q29617517 | ||
| Benchmarking sets for molecular docking | Q29619637 | ||
| GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences | Q29620735 | ||
| Knowledge-based interaction potentials for proteins. | Q30322116 | ||
| Effective energy functions for protein structure prediction. | Q30326636 | ||
| How to generate improved potentials for protein tertiary structure prediction: a lattice model study. | Q30327152 | ||
| Discrimination of near-native protein structures from misfolded models by empirical free energy functions. | Q30327412 | ||
| A distance-dependent atomic knowledge-based potential for improved protein structure selection. | Q30328397 | ||
| Statistical potentials for fold assessment. | Q30329417 | ||
| A Novel Approach to Decoy Set Generation: Designing a Physical Energy Function Having Local Minima with Native Structure Characteristics | Q30332832 | ||
| Decoys for docking | Q30350781 | ||
| An atomic environment potential for use in protein structure prediction. | Q30351317 | ||
| Novel procedure for modeling ligand/receptor induced fit effects. | Q30352584 | ||
| A new generation of statistical potentials for proteins. | Q30353364 | ||
| OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing. | Q30366976 | ||
| New statistical potential for quality assessment of protein models and a survey of energy functions | Q30386627 | ||
| Are database-derived potentials valid for scoring both forward and inverted protein folding? | Q30424713 | ||
| Empirical potentials and functions for protein folding and binding. | Q30426883 | ||
| Residue-residue mean-force potentials for protein structure recognition. | Q30429120 | ||
| An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction | Q30429585 | ||
| Influence of protein structure databases on the predictive power of statistical pair potentials | Q30430527 | ||
| Detailed analysis of scoring functions for virtual screening | Q30985789 | ||
| DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction | Q31007675 | ||
| Ligand discovery from a dopamine D3 receptor homology model and crystal structure | Q31031038 | ||
| Parameter estimation for scoring protein-ligand interactions using negative training data | Q31062677 | ||
| Molecular recognition and docking algorithms | Q31130825 | ||
| Virtual ligand screening: strategies, perspectives and limitations | Q33247681 | ||
| Molecular docking screens using comparative models of proteins | Q33511631 | ||
| Structure of chloramphenicol acetyltransferase at 1.75-A resolution | Q33580011 | ||
| The dependence of all-atom statistical potentials on structural training database | Q34185888 | ||
| Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming | Q34447822 | ||
| Further development and validation of empirical scoring functions for structure-based binding affinity prediction | Q34526566 | ||
| Lead discovery using molecular docking | Q34754631 | ||
| Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET. | Q35229337 | ||
| Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions | Q35419556 | ||
| A statistical mechanical method to optimize energy functions for protein folding | Q35667592 | ||
| Development of novel statistical potentials for protein fold recognition | Q35749519 | ||
| Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes | Q36281424 | ||
| OPUS-Ca: a knowledge-based potential function requiring only Calpha positions | Q36393213 | ||
| Lessons from the design of a novel atomic potential for protein folding | Q36476809 | ||
| Orientational potentials extracted from protein structures improve native fold recognition | Q36519520 | ||
| Prediction of protein-ligand interactions. Docking and scoring: successes and gaps | Q36605327 | ||
| Docking and scoring--theoretically easy, practically impossible? | Q36638657 | ||
| Calculation of protein-ligand binding affinities. | Q36698596 | ||
| Molecular recognition and binding free energy calculations in drug development | Q37130820 | ||
| Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction | Q38270534 | ||
| Deciphering common failures in molecular docking of ligand-protein complexes. | Q38305525 | ||
| Discovery of a cytokinin deaminase | Q38793493 | ||
| Boltzmann's principle, knowledge-based mean fields and protein folding. An approach to the computational determination of protein structures | Q40775428 | ||
| Structure-derived potentials and protein simulations | Q41052933 | ||
| Potential energy functions for threading | Q41052943 | ||
| Comparison of database potentials and molecular mechanics force fields | Q41420412 | ||
| Structure-based optimization of a non-beta-lactam lead results in inhibitors that do not up-regulate beta-lactamase expression in cell culture | Q41874068 | ||
| Enzymatic deamination of the epigenetic base N-6-methyladenine | Q42624879 | ||
| Customizing scoring functions for docking | Q42746608 | ||
| Rapid context-dependent ligand desolvation in molecular docking | Q43624519 | ||
| Comparative evaluation of 11 scoring functions for molecular docking | Q44457437 | ||
| Crystal structure of chicken liver dihydrofolate reductase complexed with NADP+ and biopterin | Q45942883 | ||
| PostDOCK: a structural, empirical approach to scoring protein ligand complexes. | Q45966259 | ||
| A practical approach to docking of zinc metalloproteinase inhibitors | Q47397132 | ||
| Assessing scoring functions for protein-ligand interactions | Q47429905 | ||
| Hydrogen bonds and salt bridges across protein-protein interfaces | Q47626463 | ||
| Factors influencing the ability of knowledge-based potentials to identify native sequence-structure matches | Q47633923 | ||
| SFCscore: scoring functions for affinity prediction of protein-ligand complexes. | Q50131043 | ||
| Novel, customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery. | Q51924901 | ||
| POEM: Parameter Optimization using Ensemble Methods: application to target specific scoring functions. | Q51963842 | ||
| Atomically detailed potentials to recognize native and approximate protein structures. | Q51967602 | ||
| SMall Molecule Growth 2001 (SMoG2001): an improved knowledge-based scoring function for protein-ligand interactions. | Q52038712 | ||
| Can a pairwise contact potential stabilize native protein folds against decoys obtained by threading? | Q52082394 | ||
| Knowledge-based scoring function to predict protein-ligand interactions. | Q52083236 | ||
| Ligand solvation in molecular docking. | Q52214239 | ||
| A general and fast scoring function for protein-ligand interactions: a simplified potential approach. | Q52222006 | ||
| Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model. | Q52230454 | ||
| A fast flexible docking method using an incremental construction algorithm. | Q52298930 | ||
| P433 | issue | 12 | |
| P921 | main subject | statistics | Q12483 |
| P304 | page(s) | 3078-3092 | |
| P577 | publication date | 2011-11-21 | |
| P1433 | published in | Journal of Chemical Information and Modeling | Q3007982 |
| P1476 | title | Statistical potential for modeling and ranking of protein-ligand interactions | |
| P478 | volume | 51 |
| Q30360125 | A Bayesian statistical approach of improving knowledge-based scoring functions for protein-ligand interactions. |
| Q91668528 | Archiving and disseminating integrative structure models |
| Q38620756 | Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions |
| Q50585647 | Coarse-graining strategy for molecular pair interactions: A reaction coordinate study for two- and three-dimensional systems. |
| Q27677025 | Crystal structure of SsfS6, the putativeC-glycosyltransferase involved in SF2575 biosynthesis |
| Q36152480 | Detecting the native ligand orientation by interfacial rigidity: SiteInterlock |
| Q42062076 | Development of the knowledge-based and empirical combined scoring algorithm (KECSA) to score protein-ligand interactions |
| Q38526679 | Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity? |
| Q35706440 | Error-prone pcr-based mutagenesis strategy for rapidly generating high-yield influenza vaccine candidates. |
| Q42056130 | Evaluation of 11 scoring functions performance on matrix metalloproteinases |
| Q30425395 | Extracting knowledge from protein structure geometry |
| Q42698956 | GaudiMM: A modular multi-objective platform for molecular modeling |
| Q40650150 | GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field |
| Q31119539 | Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets |
| Q42693973 | Integrative structure modeling with the Integrative Modeling Platform |
| Q38092807 | Latest developments in molecular docking: 2010-2011 in review |
| Q35629236 | Metagenomics reveals the high polycyclic aromatic hydrocarbon-degradation potential of abundant uncultured bacteria from chronically polluted subantarctic and temperate coastal marine environments. |
| Q30355976 | ModBase, a database of annotated comparative protein structure models and associated resources. |
| Q26800097 | Molecular docking and structure-based drug design strategies |
| Q33751096 | Molecular modeling and ligand docking for solute carrier (SLC) transporters |
| Q42862591 | Optimized atomic statistical potentials: assessment of protein interfaces and loops |
| Q35164065 | Prediction of substrates for glutathione transferases by covalent docking |
| Q34420138 | Role of Serine140 in the mode of action of Mycobacterium tuberculosis β-ketoacyl-ACP Reductase (MabA) |
| Q41852852 | The Movable Type Method Applied to Protein-Ligand Binding |
| Q48280343 | Yada: a novel tool for molecular docking calculations. |
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