Benchmarking Sets for Molecular Docking

Huang, Niu; Shoichet, Brian K.; Irwin, John J.

doi:10.1021/jm0608356

Cited by 1,199 publications

(1,684 citation statements)

References 88 publications

(207 reference statements)

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“…The authors of this study reported an improvement of correlation coefficient between calculated and experimental binding energies from 0.347 for the AutoDock calculated binding energies to 0.996 after rescoring. A reason for the lack of improvement in the current study may be the challenging DUD decoy set, which was chosen according to physico-chemical similarity with the known ligands (Huang et al, 2006).…”

Section: Resultsmentioning

confidence: 99%

“…The present study reveals the first evaluation of the virtual screening performance of the new software AutoDock Vina (Trott & Olson, 2010), the new version of AutoDock 4.2 (Huey et al, 2007) and Gemdock (Yang & Chen, 2004) against a selection of targets from the Database of Useful Decoys (Huang et al, 2006) (DUD). In addition, various strategies of combining the results of two or more docking algorithms were developed and and the utility of recently published ligand efficiency indices (Garcia-Sosa et al, 2010) was evaluated.…”

Section: Introductionmentioning

confidence: 99%

“…DUD contains protein targets with known ligands and decoys that have similar physico-chemical properties to known ligands; thus it provides a challenging test case for virtual screening methods. The performance of virtual screening methods depends on the protein target, therefore, for this work ten targets were selected based on previously reported enrichment factors (Huang et al, 2006) providing a combination of challenging and easy targets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Consensus virtual screening approaches to predict protein ligands

Kukol

2011

European Journal of Medicinal Chemistry

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-In order to exploit the advantages of receptor-based virtual screening, namely time/cost saving and specificity, it is important to rely on algorithms that predict a high number of active ligands at the top ranks of a small molecule database. Towards that goal consensus methods combining the results of several docking algorithms were developed and compared against the individual algorithms. Furthermore, a recently proposed rescoring method based on drug efficiency indices was evaluated. Among AutoDock Vina 1.0, AutoDock 4.2 and GemDock, AutoDock Vina was the best performing single method in predicting high affinity ligands from a database of known ligands and decoys. The rescoring of predicted binding energies with the water/butanol partition coeffcient did not lead to an improvement averaged over all receptor targets. Various consensus algorithms were investigated and a simple combination of AutoDock and AutoDock Vina results gave the most consistent performance that showed early enrichment of known ligands for all receptor targets investigated. In case a number ligands is known for a specific target, every method proposed in this study should be evaluated.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Consensus virtual screening approaches to predict protein ligands

Kukol

2011

European Journal of Medicinal Chemistry

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“…Furthermore, MQN-calculations are extremely fast and therefore particularly well suited to classify very large databases such as PubChem, which contains over 20 million structures, or the chemical universe database GDB-13 which contains 977 million structures as shown in the following paper in this issue [22][23][24][25]. The MQN-system is also relevant for medicinal chemistry, as illustrated by the fact that similarity searches in MQN-space enrich bioactives in the DUD database [26] from the entire PubChem with efficiencies comparable to that of substructure fingerprints, with the added benefit that lead-hopping relationships between actives are allowed [27].…”

Section: Introductionmentioning

confidence: 99%

Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem

Deursen

Blum

Reymond

2011

J Comput Aided Mol Des

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The 4.5 million organic molecules with up to 20 non-hydrogen atoms in PubChem were analyzed using the MQN-system, which consists in 42 integer value descriptors of molecular structure. The 42-dimensional MQN-space was visualised by principal component analysis and representation of the (PC1, PC2), (PC1, PC3) and (PC2, PC3) planes. The molecules were organized according to ring count (PC1, 38% of variance), the molecular size (PC2, 25% of variance), and the H-bond acceptor count (PC3, 12% of variance). Compounds following Lipinski's bioavailability, Oprea's lead-likeness and Congreve's fragment-likeness criteria formed separated groups in MQN-space visible in the (PC2, PC3) plane. MQN-similarity searches of the 4.5 million molecules (see the browser available at www.gdb.unibe.ch) gave significant enrichment factors for recovering groups of fragment-sized bioactive compounds related to ten different biological targets taken from Chembl, allowing leadhopping relationships not seen with substructure fingerprint similarity searches. The diversity of different compound series was analyzed by MQN-distance histograms.

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“…Here, we selected the Directory of Universal Decoys (DUD) [30] as the source of decoys. In the DUD, there are a total of 15996 decoys that are believed to be non-binders of EGFR TK.…”

Section: Ligand Preparationmentioning

confidence: 99%

Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors

Sun

Chen

et al. 2013

Acta Pharmacol Sin

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Aim: To develop a novel 3D-QSAR approach for study of the epidermal growth factor receptor tyrosine kinase (EGFR TK) and its inhibitors. Methods: One hundred thirty nine EGFR TK inhibitors were classified into 3 clusters. Ensemble docking of these inhibitors with 19 EGFR TK crystal structures was performed. Three protein structures that showed the best recognition of each cluster were selected based on the docking results. Then, a novel QSAR (ensemble-QSAR) building method was developed based on the ligand conformations determined by the corresponding protein structures. Results: Compared with the 3D-QSAR model, in which the ligand conformations were determined by a single protein structure, ensemble-QSAR exhibited higher R2 (0.87) and Q2 (0.78) values and thus appeared to be a more reliable and better predictive model. Ensemble-QSAR was also able to more accurately describe the interactions between the target and the ligands. Conclusion: The novel ensemble-QSAR model built in this study outperforms the traditional 3D-QSAR model in rationality, and provides a good example of selecting suitable protein structures for docking prediction and for building structure-based QSAR using available protein structures.

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Benchmarking Sets for Molecular Docking

Cited by 1,199 publications

References 88 publications

Consensus virtual screening approaches to predict protein ligands

Consensus virtual screening approaches to predict protein ligands

Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem

Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors

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