| P2093 | author name string | Gerhard Klebe | |
| P2860 | cites work | All are not equal: a benchmark of different homology modeling programs | Q24644563 |
| | pH-dependent binding modes observed in trypsin crystals: lessons for structure-based drug design | Q27638635 |
| | Successful virtual screening for novel inhibitors of human carbonic anhydrase: strategy and experimental confirmation | Q27639470 |
| | Reconstructing the binding site of factor Xa in trypsin reveals ligand-induced structural plasticity | Q27640311 |
| | Virtual screening for submicromolar leads of tRNA-guanine transglycosylase based on a new unexpected binding mode detected by crystal structure analysis | Q27640724 |
| | Understanding protein-ligand interactions: the price of protein flexibility | Q27642968 |
| | ZINC--a free database of commercially available compounds for virtual screening | Q27656255 |
| | Rational design of potent, bioavailable, nonpeptide cyclic ureas as HIV protease inhibitors | Q27731528 |
| | Structure of a non-peptide inhibitor complexed with HIV-1 protease. Developing a cycle of structure-based drug design | Q27731962 |
| | Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings | Q27861111 |
| | Hit and lead generation: beyond high-throughput screening | Q28204526 |
| | Principles of docking: An overview of search algorithms and a guide to scoring functions | Q28217048 |
| | Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors | Q28219491 |
| | Development and validation of a genetic algorithm for flexible docking | Q28236574 |
| | Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy | Q28251042 |
| | The particle concept: placing discrete water molecules during protein-ligand docking predictions. | Q52214244 |
| | A fast flexible docking method using an incremental construction algorithm. | Q52298930 |
| | Ligand docking to proteins with discrete side-chain flexibility | Q52383197 |
| | DrugScore meets CoMFA: adaptation of fields for molecular comparison (AFMoC) or how to tailor knowledge-based pair-potentials to a particular protein | Q52413367 |
| | A bioavailability score | Q53622895 |
| | Virtual screening using protein-ligand docking: avoiding artificial enrichment | Q53639582 |
| | Fragment-Based Drug Discovery | Q55934078 |
| | How many leads from HTS? | Q56627226 |
| | Current trends in lead discovery: are we looking for the appropriate properties? | Q57017035 |
| | Principles of docking: An overview of search algorithms and a guide to scoring functions | Q57808017 |
| | A new grammar for drug discovery | Q59052529 |
| | Comparative docking studies on ligand binding to the multispecific antibodies IgE-La2 and IgE-Lb4 | Q71699017 |
| | How many leads from HTS? - Comment | Q73396831 |
| | Evaluation of the CASP2 docking section | Q74252620 |
| | From the analyst's couch. Trends in development cycles | Q74468330 |
| | Docking into knowledge-based potential fields: a comparative evaluation of DrugScore | Q78031711 |
| | Expect the unexpected or caveat for drug designers: multiple structure determinations using aldose reductase crystals treated under varying soaking and co-crystallisation conditions | Q80212010 |
| | Incorporating protein flexibility in structure-based drug discovery: using HIV-1 protease as a test case | Q80850175 |
| | Improving binding mode predictions by docking into protein-specifically adapted potential fields | Q81077622 |
| | Virtual exploration of the small-molecule chemical universe below 160 Daltons | Q81327462 |
| | Structure-based drug discovery using GPCR homology modeling: successful virtual screening for antagonists of the alpha1A adrenergic receptor | Q81409742 |
| | Utilising structural knowledge in drug design strategies: applications using Relibase | Q52023426 |
| | Similarity-driven flexible ligand docking | Q52076419 |
| | Knowledge-based scoring function to predict protein-ligand interactions | Q52083236 |
| | Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins | Q52132563 |
| | SuperStar: a knowledge-based approach for identifying interaction sites in proteins | Q52211818 |
| | Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 protease | Q28331230 |
| | The art and practice of structure-based drug design: A molecular modeling perspective | Q29029768 |
| | The druggable genome | Q29547361 |
| | Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening | Q29617343 |
| | FlexE: efficient molecular docking considering protein structure variations. | Q30328164 |
| | Utility of homology models in the drug discovery process. | Q30342415 |
| | Decoys for docking | Q30350781 |
| | A computational procedure for determining energetically favorable binding sites on biologically important macromolecules | Q30406755 |
| | High-throughput docking for lead generation | Q30663788 |
| | Chemical feature-based pharmacophores and virtual library screening for discovery of new leads. | Q30780748 |
| | Comparing Performance of Computational Tools for Combinatorial Library Design | Q30824935 |
| | Detailed analysis of scoring functions for virtual screening | Q30985789 |
| | Druggability indices for protein targets derived from NMR-based screening data | Q30986175 |
| | Pharmacophore modeling and three-dimensional database searching for drug design using catalyst | Q30997393 |
| | McMaster University data-mining and docking competition: computational models on the catwalk | Q31005544 |
| | Experimental screening of dihydrofolate reductase yields a "test set" of 50,000 small molecules for a computational data-mining and docking competition | Q31005550 |
| | 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 |
| | How does consensus scoring work for virtual library screening? An idealized computer experiment | Q31017163 |
| | Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. | Q31057078 |
| | Validation of an empirical RNA-ligand scoring function for fast flexible docking using Ribodock | Q31112340 |
| | Virtual screening to enrich hit lists from high-throughput screening: a case study on small-molecule inhibitors of angiogenin. | Q31120824 |
| | Molecular recognition and docking algorithms | Q31130825 |
| | Integration of virtual screening into the drug discovery process | Q31134010 |
| | Binding site characteristics in structure-based virtual screening: evaluation of current docking tools | Q31135121 |
| | Docking ligands onto binding site representations derived from proteins built by homology modelling. | Q32181064 |
| | High throughput screening identifies novel inhibitors of Escherichia coli dihydrofolate reductase that are competitive with dihydrofolate | Q33187699 |
| | Drug-like annotation and duplicate analysis of a 23-supplier chemical database totalling 2.7 million compounds | Q33199870 |
| | Comparative evaluation of eight docking tools for docking and virtual screening accuracy | Q33206310 |
| | Virtual screening of chemical libraries | Q33209939 |
| | Virtual ligand screening against Escherichia coli dihydrofolate reductase: improving docking enrichment using physics-based methods | Q33223542 |
| | Screening for dihydrofolate reductase inhibitors using MOLPRINT 2D, a fast fragment-based method employing the naïve Bayesian classifier: limitations of the descriptor and the importance of balanced chemistry in training and test sets | Q33223543 |
| | Here be dragons: docking and screening in an uncharted region of chemical space | Q33223544 |
| | Comparison of automated docking programs as virtual screening tools | Q33389993 |
| | Docking: successes and challenges | Q33389996 |
| | Isothermal titration calorimetry and differential scanning calorimetry as complementary tools to investigate the energetics of biomolecular recognition | Q33682917 |
| | Accommodating protein flexibility in computational drug design | Q33825095 |
| | A ‘Rule of Three’ for fragment-based lead discovery? | Q33973131 |
| | A geometric approach to macromolecule-ligand interactions | Q34280176 |
| | Testing a flexible-receptor docking algorithm in a model binding site | Q34309041 |
| | Docking and scoring in virtual screening for drug discovery: methods and applications | Q34364227 |
| | Lead discovery using molecular docking | Q34754631 |
| | Structure-based virtual screening: an overview | Q35053155 |
| | Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions | Q35057378 |
| | ADMET in silico modelling: towards prediction paradise? | Q35075770 |
| | Implications of protein flexibility for drug discovery | Q35167908 |
| | Recent development and application of virtual screening in drug discovery: an overview | Q35743496 |
| | Virtual screening methods that complement HTS. | Q35807279 |
| | Fragment-based lead discovery | Q35852295 |
| | Target-biased scoring approaches and expert systems in structure-based virtual screening | Q35853042 |
| | High-throughput docking as a source of novel drug leads | Q35853047 |
| | Virtual screening in lead discovery and optimization. | Q35875498 |
| | Virtual screening in structure-based drug discovery | Q35892808 |
| | Comparing protein-ligand docking programs is difficult. | Q36152993 |
| | Identification of ligands for RNA targets via structure-based virtual screening: HIV-1 TAR. | Q41751942 |
| | Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes | Q42602259 |
| | Inhibitors of dihydrodipicolinate reductase, a key enzyme of the diaminopimelate pathway of Mycobacterium tuberculosis. | Q43600476 |
| | Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock | Q43825573 |
| | Flexible docking under pharmacophore type constraints | Q44109232 |
| | Docking flexible ligands to macromolecular receptors by molecular shape | Q44113013 |
| | Structural parameterization of the binding enthalpy of small ligands | Q44125121 |
| | Protein-based virtual screening of chemical databases. II. Are homology models of G-Protein Coupled Receptors suitable targets? | Q44242205 |
| | Pharmacophore-based molecular docking to account for ligand flexibility | Q44379444 |
| | Comparative evaluation of 11 scoring functions for molecular docking | Q44457437 |
| | ZZ made EZ: influence of inhibitor configuration on enzyme selectivity. | Q44505963 |
| | Crystallographic study of inhibitors of tRNA-guanine transglycosylase suggests a new structure-based pharmacophore for virtual screening | Q44819422 |
| | Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model | Q45104890 |
| | Evaluating the high-throughput screening computations | Q46186021 |
| | Unveiling the full potential of flexible receptor docking using multiple crystallographic structures | Q46563192 |
| | Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes | Q46586108 |
| | Molecular docking to ensembles of protein structures | Q46668232 |
| | Modeling water molecules in protein-ligand docking using GOLD. | Q46725683 |
| | A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance | Q47307422 |
| | Ligand-supported Homology Modelling of Protein Binding-sites using Knowledge-based Potentials | Q47396241 |
| | Assessing scoring functions for protein-ligand interactions | Q47429905 |
| | Probing flexibility and "induced-fit" phenomena in aldose reductase by comparative crystal structure analysis and molecular dynamics simulations | Q47430898 |
| | Chris Lipinski discusses life and chemistry after the Rule of Five | Q48705443 |
| | Feature trees: a new molecular similarity measure based on tree matching | Q51647018 |
| P433 | issue | 13-14 | |
| P304 | page(s) | 580-594 | |
| P577 | publication date | 2006-07-01 | |
| P13046 | publication type of scholarly work | review article | Q7318358 |
| P1433 | published in | Drug Discovery Today | Q3040085 |
| P1476 | title | Virtual ligand screening: strategies, perspectives and limitations | |
| P478 | volume | 11 | |