| scholarly article | Q13442814 |
| review article | Q7318358 |
| P6179 | Dimensions Publication ID | 1014180785 |
| P356 | DOI | 10.1038/NRD3139 |
| P3181 | OpenCitations bibliographic resource ID | 2141640 |
| P698 | PubMed publication ID | 20357802 |
| P50 | author | Gisbert Schneider | Q51615601 |
| P2860 | cites work | Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases | Q24618939 |
| Flexible ligand docking to multiple receptor conformations: a practical alternative | Q24650066 | ||
| ZINC--a free database of commercially available compounds for virtual screening | Q27656255 | ||
| Click Chemistry: Diverse Chemical Function from a Few Good Reactions | Q28204398 | ||
| Virtual screening strategies in drug discovery | Q28253252 | ||
| Chemogenomics: an emerging strategy for rapid target and drug discovery | Q28260664 | ||
| Virtual screening for the discovery of bioactive natural products | Q28261293 | ||
| Community benchmarks for virtual screening | Q28268675 | ||
| From virtuality to reality - Virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective | Q28286079 | ||
| Computational chemistry-driven decision making in lead generation | Q28297397 | ||
| Network pharmacology: the next paradigm in drug discovery | Q28297997 | ||
| Recommendations for evaluation of computational methods | Q28755020 | ||
| Natural products as sources of new drugs over the last 25 years | Q29615119 | ||
| Long-timescale molecular dynamics simulations of protein structure and function. | Q30376113 | ||
| What do we know and when do we know it? | Q30481529 | ||
| Novel paradigms for drug discovery: computational multitarget screening | Q30662767 | ||
| Drug discovery and natural products: end of an era or an endless frontier? | Q30866998 | ||
| Virtual ligand screening: strategies, perspectives and limitations | Q33247681 | ||
| Virtual screening in drug discovery -- a computational perspective | Q33294074 | ||
| Biology-inspired synthesis of compound libraries | Q33314572 | ||
| Predictive QSAR modeling workflow, model applicability domains, and virtual screening | Q33316534 | ||
| Similarity searching and scaffold hopping in synthetically accessible combinatorial chemistry spaces | Q33326449 | ||
| Recent developments in de novo design and scaffold hopping | Q33329842 | ||
| Natural products in drug discovery | Q33359012 | ||
| Scaffold diversity of natural products: inspiration for combinatorial library design | Q33372355 | ||
| Voyages to the (un)known: adaptive design of bioactive compounds | Q33384049 | ||
| In silico prediction of drug properties | Q33400509 | ||
| Self-organizing maps in drug discovery: compound library design, scaffold-hopping, repurposing | Q33400511 | ||
| Distilling free-form natural laws from experimental data | Q33426258 | ||
| Virtual screening - what does it give us? | Q33435433 | ||
| Recent advances in computer-aided drug design | Q33443633 | ||
| The importance of discerning shape in molecular pharmacology | Q33789461 | ||
| Computer-based de novo design of drug-like molecules | Q34438895 | ||
| Protein simulation and drug design | Q35590732 | ||
| Combinatorial thinking in chemistry and biology | Q36149745 | ||
| Assessment of structural diversity in combinatorial synthesis | Q36153692 | ||
| Scoring functions--the first 100 years. | Q36289042 | ||
| Chemogenomics in drug discovery | Q36481926 | ||
| Designing ligands to bind proteins | Q36526030 | ||
| Calculation of protein-ligand binding affinities. | Q36698596 | ||
| Recent advances in drug design methods: where will they lead? | Q36728851 | ||
| Do enthalpy and entropy distinguish first in class from best in class? | Q36967464 | ||
| Virtual screening and its integration with modern drug design technologies. | Q37066134 | ||
| Theoretical and practical considerations in virtual screening: a beaten field? | Q37066144 | ||
| Essential factors for successful virtual screening | Q37066816 | ||
| Grid computing in large pharmaceutical molecular modeling | Q37206439 | ||
| Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening | Q37219517 | ||
| Synergies of virtual screening approaches | Q37240026 | ||
| Efficient drug lead discovery and optimization | Q37307959 | ||
| Managing protein flexibility in docking and its applications | Q37382489 | ||
| Distance phenomena in high-dimensional chemical descriptor spaces: consequences for similarity-based approaches | Q37407961 | ||
| Machine learning in virtual screening. | Q37481566 | ||
| How wrong can we get? A review of machine learning approaches and error bars | Q37515537 | ||
| SFCscore: scoring functions for affinity prediction of protein-ligand complexes. | Q50131043 | ||
| Anton, a special-purpose machine for molecular dynamics simulation | Q56019494 | ||
| A Synthesis Strategy Yielding Skeletally Diverse Small Molecules Combinatorially | Q56833649 | ||
| Thinking outside the flask | Q73912945 | ||
| Inhibitors of HIV-1 protease by using in situ click chemistry | Q82316996 | ||
| P433 | issue | 4 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | virtual screening | Q4112105 |
| P304 | page(s) | 273-6 | |
| P577 | publication date | 2010-04-01 | |
| P1433 | published in | Nature Reviews Drug Discovery | Q45998 |
| P1476 | title | Virtual screening: an endless staircase? | |
| P478 | volume | 9 |
| Q40276857 | 3D-RISM-Dock: A New Fragment-Based Drug Design Protocol |
| Q39425242 | A D3R prospective evaluation of machine learning for protein-ligand scoring. |
| Q89808093 | A computational approach yields selective inhibitors of human excitatory amino acid transporter 2 (EAAT2) |
| Q40230609 | A simple click by click protocol to perform docking: AutoDock 4.2 made easy for non-bioinformaticians |
| Q53638590 | A steered molecular dynamics mediated hit discovery for histone deacetylases. |
| Q38750215 | Active learning for computational chemogenomics |
| Q90405888 | Add-On therapy with Chinese herb medicine Bo-Er-Ning capsule (BENC) improves outcomes of gastric cancer patients: a randomized clinical trial followed with bioinformatics-assisted mechanism study |
| Q30427576 | Alchemical free energy methods for drug discovery: progress and challenges |
| Q42238354 | Anti-infectious drug repurposing using an integrated chemical genomics and structural systems biology approach. |
| Q92313646 | Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery |
| Q50111842 | Application of computational methods for anticancer drug discovery, design, and optimization. |
| Q27902289 | Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization |
| Q35182375 | Atomistic account of structural and dynamical changes induced by small binders in the double helix of a short DNA. |
| Q52659131 | Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. |
| Q35374554 | Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis |
| Q34632430 | Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery |
| Q89556737 | Beyond chimerism analysis: methods for tracking a new generation of cell-based medicines |
| Q34516992 | Binding modes of peptidomimetics designed to inhibit STAT3. |
| Q28478269 | Bioassays to monitor Taspase1 function for the identification of pharmacogenetic inhibitors |
| Q39127424 | Biobetters From an Integrated Computational/Experimental Approach. |
| Q90295745 | Blinded prediction of protein-ligand binding affinity using Amber thermodynamic integration for the 2018 D3R grand challenge 4 |
| Q28072766 | Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research |
| Q24810481 | Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases |
| Q36578335 | ChemProt-3.0: a global chemical biology diseases mapping |
| Q37094597 | Chemically Advanced Template Search (CATS) for Scaffold-Hopping and Prospective Target Prediction for 'Orphan' Molecules |
| Q48185450 | Classifiers and their Metrics Quantified. |
| Q41547898 | Cloud computing approaches for prediction of ligand binding poses and pathways. |
| Q28709665 | Combining cheminformatics methods and pathway analysis to identify molecules with whole-cell activity against Mycobacterium tuberculosis |
| Q34973773 | Computational approaches for designing potent and selective analogs of peptide toxins as novel therapeutics |
| Q89662547 | Computational approaches for drug discovery against trypanosomatid-caused diseases |
| Q34153280 | Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery |
| Q44083547 | Computational models for neglected diseases: gaps and opportunities |
| Q47889903 | Computer aided drug design and its application to the development of potential drugs for neurodegenerative disorders |
| Q58736657 | Conformational analysis of a polyconjugated protein-binding ligand by joint quantum chemistry and polarizable molecular mechanics. Addressing the issues of anisotropy, conjugation, polarization, and multipole transferability |
| Q42212503 | Considerations of Protein Subpockets in Fragment-Based Drug Design |
| Q28492136 | Covalent docking predicts substrates for haloalkanoate dehalogenase superfamily phosphatases |
| Q52654614 | Current therapeutic molecules and targets in neurodegenerative diseases based on in silico drug design. |
| Q41931663 | Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets. |
| Q35101950 | DINC: a new AutoDock-based protocol for docking large ligands. |
| Q92407042 | De novo generation of hit-like molecules from gene expression signatures using artificial intelligence |
| Q52855112 | Deciphering the EU clinical trials regulation. |
| Q39034707 | Design and synthesis of anti-cancer cyclopeptides containing triazole skeleton |
| Q39681302 | Designing the molecular future |
| Q40335742 | Discovery of Cdc25A Lead Inhibitors with a Novel Chemotype by Virtual Screening: Application of Pharmacophore Modeling Based on a Training Set with a Limited Number of Unique Components |
| Q35542998 | Discovery of N-[4-(1H-Pyrazolo[3,4-b]pyrazin-6-yl)-phenyl]-sulfonamides as Highly Active and Selective SGK1 Inhibitors |
| Q42364599 | Discovery of a Low Toxicity O-GlcNAc Transferase (OGT) Inhibitor by Structure-based Virtual Screening of Natural Products |
| Q28541741 | Discovery of a natural product-like iNOS inhibitor by molecular docking with potential neuroprotective effects in vivo |
| Q34583298 | Discovery of novel biomarkers and phenotypes by semantic technologies |
| Q28543793 | Discovery of novel new Delhi metallo-β-lactamases-1 inhibitors by multistep virtual screening |
| Q34249476 | Discovery of novel-scaffold monoamine transporter ligands via in silico screening with the S1 pocket of the serotonin transporter |
| Q37996113 | Discovery of small molecule cancer drugs: successes, challenges and opportunities |
| Q42701435 | Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology |
| Q45961578 | Editorial: Charting Chemical Space: Challenges and Opportunities for Artificial Intelligence and Machine Learning. |
| Q34046987 | Efficient hit-finding approaches for histone methyltransferases: the key parameters |
| Q28487748 | Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models |
| Q30959752 | Enrichment assessment of multiple virtual screening strategies for Toll-like receptor 8 agonists based on a maximal unbiased benchmarking data set. |
| Q34011410 | Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design |
| Q51711644 | European regulation on orphan medicinal products: 10 years of experience and future perspectives. |
| Q31011332 | Exhaustive sampling of docking poses reveals binding hypotheses for propafenone type inhibitors of P-glycoprotein |
| Q27144201 | Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning |
| Q47373683 | Focused conformational sampling in proteins. |
| Q45961575 | From Hits to Leads: Challenges for the Next Phase of Machine Learning in Medicinal Chemistry. |
| Q38201038 | Fusing similarity rankings in ligand-based virtual screening |
| Q57162600 | G-quadruplex virtual drug screening: A review |
| Q47626065 | Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point |
| Q34397745 | Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification |
| Q36795770 | How Reliable Are Ligand-Centric Methods for Target Fishing? |
| Q28478621 | Hydrogen bonding penalty upon ligand binding |
| Q50868368 | Identification of 1H-indene-(1,3,5,6)-tetrol derivatives as potent pancreatic lipase inhibitors using molecular docking and molecular dynamics approach. |
| Q36708826 | Identification of HIV inhibitors guided by free energy perturbation calculations |
| Q34360715 | Identification of UV-protective activators of nuclear factor erythroid-derived 2-related factor 2 (Nrf2) by combining a chemical library screen with computer-based virtual screening |
| Q39348639 | Identification of novel polo-like kinase 1 inhibitors by a hybrid virtual screening |
| Q30845301 | Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis. |
| Q30409188 | Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics |
| Q64092935 | ImmtorLig_DB: repertoire of virtually screened small molecules against immune receptors to bolster host immunity |
| Q31119539 | Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets |
| Q28834223 | Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines |
| Q33600249 | In Silico Identification of Mimicking Molecules as Defense Inducers Triggering Jasmonic Acid Mediated Immunity against Alternaria Blight Disease in Brassica Species. |
| Q56992142 | Interspecies comparison of putative ligand binding sites of human, rat and mouse P-glycoprotein |
| Q37701430 | LIBSA--a method for the determination of ligand-binding preference to allosteric sites on receptor ensembles |
| Q84618926 | Lead identification: combining strengths to find high-quality leads |
| Q91942928 | Ligand biological activity predicted by cleaning positive and negative chemical correlations |
| Q35801074 | Ligand similarity guided receptor selection enhances docking accuracy and recall for non-nucleoside HIV reverse transcriptase inhibitors |
| Q114969981 | MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning |
| Q57832782 | MOLECULAR SIMILARITY ANALYSIS |
| Q35765681 | Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space |
| Q35777486 | Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations |
| Q28545910 | Machine learning assisted design of highly active peptides for drug discovery |
| Q26750642 | Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening |
| Q36138895 | Matrix-based Molecular Descriptors for Prospective Virtual Compound Screening |
| Q37855896 | Modeling approaches for ligand-based 3D similarity. |
| Q26784325 | Modeling enzyme-ligand binding in drug discovery |
| Q47422914 | Molecular rigidity and enthalpy-entropy compensation in DNA melting |
| Q38727295 | Multiple search methods for similarity-based virtual screening: analysis of search overlap and precision |
| Q48140269 | N1-Selective alkenylation of 1-sulfonyl-1,2,3-triazoles with alkynes via gold catalysis |
| Q90012518 | New Trends in Virtual Screening |
| Q27667324 | Novel Ligands for a Purine Riboswitch Discovered by RNA-Ligand Docking |
| Q37858324 | Old friends in new guise: repositioning of known drugs with structural bioinformatics |
| Q42659257 | One Size Does Not Fit All: The Limits of Structure-Based Models in Drug Discovery |
| Q34625066 | Optimization of molecular docking scores with support vector rank regression |
| Q37524043 | Optimized CGenFF force-field parameters for acylphosphate and N-phosphonosulfonimidoyl functional groups |
| Q47356901 | Overcoming antibiotic resistance: Is siderophore Trojan horse conjugation an answer to evolving resistance in microbial pathogens? |
| Q62630863 | Peptides for Infectious Diseases: From Probe Design to Diagnostic Microarrays |
| Q38821300 | Performance of machine-learning scoring functions in structure-based virtual screening |
| Q36092217 | Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity |
| Q28822337 | Plate-based diversity subset screening generation 2: an improved paradigm for high-throughput screening of large compound files |
| Q34655968 | Plate-based diversity subset screening: an efficient paradigm for high throughput screening of a large screening file |
| Q34668264 | Polyphony: superposition independent methods for ensemble-based drug discovery |
| Q90331962 | Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation |
| Q38272815 | Predicting the effects of basepair mutations in DNA-protein complexes by thermodynamic integration. |
| Q35073109 | Predictive power of molecular dynamics receptor structures in virtual screening |
| Q57794249 | Protein design: from computer models to artificial intelligence |
| Q56084686 | PubChem chemical structure standardization |
| Q33850364 | PubChem3D: Diversity of shape |
| Q33858671 | Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. |
| Q33893113 | Recent trends and observations in the design of high-quality screening collections |
| Q37403328 | Reengineering translational science: the time is right |
| Q28083593 | Review: basics of drug development in rheumatology |
| Q96834988 | SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design |
| Q35878687 | Selecting, Acquiring, and Using Small Molecule Libraries for High-Throughput Screening |
| Q91708289 | Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins |
| Q34454041 | Significance estimation for sequence-based chemical similarity searching (PhAST) and application to AuroraA kinase inhibitors |
| Q34360291 | Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization. |
| Q36677835 | Structural basis of resistance to anti-cytochrome bc₁ complex inhibitors: implication for drug improvement |
| Q36083075 | Structure-Based Screening of Uncharted Chemical Space for Atypical Adenosine Receptor Agonists. |
| Q36267055 | Structure-Based Virtual Screening |
| Q42121557 | Structure-based predictions of activity cliffs |
| Q27022502 | Structure-based virtual screening for drug discovery: a problem-centric review |
| Q55669302 | Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein–Ligand Complexes in Cognate Docking. |
| Q26823990 | Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors |
| Q47908788 | The evaluation of QM/MM-driven molecular docking combined with MM/GBSA calculations as a halogen-bond scoring strategy |
| Q28275104 | USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques |
| Q28307816 | Ultrafast shape recognition: method and applications |
| Q63246359 | Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit |
| Q28480572 | Using functional signatures to identify repositioned drugs for breast, myelogenous leukemia and prostate cancer |
| Q33891020 | Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets |
| Q28068082 | Virtual Screening Approaches towards the Discovery of Toll-Like Receptor Modulators |
| Q38244681 | Virtual screening strategies: recent advances in the identification and design of anti-cancer agents |
Search more.