| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/jcamd/Jain09 |
| P6179 | Dimensions Publication ID | 1027561982 |
| P356 | DOI | 10.1007/S10822-009-9266-3 |
| P2888 | exact match | https://scigraph.springernature.com/pub.10.1007/s10822-009-9266-3 |
| P932 | PMC publication ID | 2693357 |
| P698 | PubMed publication ID | 19340588 |
| P5875 | ResearchGate publication ID | 24252200 |
| P2093 | author name string | Ajay N Jain | |
| P2860 | cites work | Diverse, high-quality test set for the validation of protein-ligand docking performance. | Q55043208 |
| Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities | Q71851460 | ||
| Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine | Q28207735 | ||
| Development and validation of a genetic algorithm for flexible docking | Q28236574 | ||
| Community benchmarks for virtual screening | Q28268675 | ||
| Scoring functions for protein-ligand docking | Q28271137 | ||
| Automated docking of flexible ligands: applications of AutoDock | Q28285420 | ||
| Recommendations for evaluation of computational methods | Q28755020 | ||
| A critical assessment of docking programs and scoring functions | Q29616761 | ||
| Benchmarking sets for molecular docking | Q29619637 | ||
| Novel procedure for modeling ligand/receptor induced fit effects. | Q30352584 | ||
| Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy. | Q30365356 | ||
| Evaluating docking programs: keeping the playing field level. | Q30367162 | ||
| A shape-based machine learning tool for drug design | Q30418333 | ||
| What do we know and when do we know it? | Q30481529 | ||
| Parameter estimation for scoring protein-ligand interactions using negative training data | Q31062677 | ||
| Comparative evaluation of eight docking tools for docking and virtual screening accuracy | Q33206310 | ||
| A geometric approach to macromolecule-ligand interactions | Q34280176 | ||
| Testing a flexible-receptor docking algorithm in a model binding site | Q34309041 | ||
| The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure | Q34326042 | ||
| An improved relaxed complex scheme for receptor flexibility in computer-aided drug design | Q36838120 | ||
| A new class of models for computing receptor-ligand binding affinities | Q41504987 | ||
| Bias, reporting, and sharing: computational evaluations of docking methods | Q42639520 | ||
| Customizing scoring functions for docking | Q42746608 | ||
| Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock | Q43825573 | ||
| Computational drug design accommodating receptor flexibility: the relaxed complex scheme | Q43992973 | ||
| Enhanced docking with the mining minima optimizer: acceleration and side-chain flexibility | Q44190842 | ||
| Ligand-based structural hypotheses for virtual screening | Q44400932 | ||
| Protein flexibility in ligand docking and virtual screening to protein kinases. | Q44786754 | ||
| Protein-ligand docking against non-native protein conformers | Q44921180 | ||
| Surflex-Dock 2.1: robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search | Q46358770 | ||
| Fast structure-based virtual ligand screening combining FRED, DOCK, and Surflex | Q46702590 | ||
| A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance | Q47307422 | ||
| Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0. | Q51922137 | ||
| Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. | Q52069951 | ||
| A fast flexible docking method using an incremental construction algorithm. | Q52298930 | ||
| Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. | Q52304046 | ||
| Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. | Q53678858 | ||
| P433 | issue | 6 | |
| P304 | page(s) | 355-374 | |
| P577 | publication date | 2009-04-02 | |
| P1433 | published in | Journal of Computer - Aided Molecular Design | Q15766522 |
| P1476 | title | Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation | |
| P478 | volume | 23 |
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| Q28256939 | Automated docking screens: a feasibility study |
| Q53076305 | Biased retrieval of chemical series in receptor-based virtual screening. |
| Q30543685 | CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series |
| Q37848281 | Challenges and advances in computational docking: 2009 in review |
| Q35453774 | Chemical and protein structural basis for biological crosstalk between PPARα and COX enzymes |
| Q51703673 | Chemical space sampling by different scoring functions and crystal structures. |
| Q89662547 | Computational approaches for drug discovery against trypanosomatid-caused diseases |
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| Q38909972 | ForceGen 3D structure and conformer generation: from small lead-like molecules to macrocyclic drugs |
| Q91763848 | Getting Docking into Shape Using Negative Image-Based Rescoring |
| Q38088365 | High-throughput and in silico screenings in drug discovery. |
| Q30391029 | In Pursuit of Fully Flexible Protein-Ligand Docking: Modeling the Bilateral Mechanism of Binding |
| Q35621988 | Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock |
| Q93126855 | Molecular Docking: Shifting Paradigms in Drug Discovery |
| Q37843834 | Molecular motions in drug design: the coming age of the metadynamics method |
| Q35046420 | Multiple-targeting and conformational selection in the estrogen receptor: computation and experiment |
| Q39513411 | PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation |
| Q30380676 | Physical binding pocket induction for affinity prediction. |
| Q39647348 | Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. |
| Q47584237 | ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research. |
| Q26864013 | Protein flexibility in docking and surface mapping |
| Q42414721 | Protein function annotation by local binding site surface similarity |
| Q43891873 | Role of interactions and volume variation in discriminating active and inactive forms of cyclin-dependent kinase-2 inhibitor complexes |
| Q42054429 | Surface-based protein binding pocket similarity |
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| Q57798700 | Targeting of Hematologic Malignancies with PTC299, A Novel Potent Inhibitor of Dihydroorotate Dehydrogenase with Favorable Pharmaceutical Properties |
| Q30379556 | Ultrafast protein structure-based virtual screening with Panther. |
| Q31109061 | Using RosettaLigand for small molecule docking into comparative models |
| Q35762844 | p38α MAPK and Type I Inhibitors: Binding Site Analysis and Use of Target Ensembles in Virtual Screening. |
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