“Scaffold-Hopping” by Topological Pharmacophore Search: A Contribution to Virtual Screening

Schneider, Gisbert; Neidhart, Werner; Giller, Thomas; Schmid, Gerard M.

doi:10.1002/(sici)1521-3773(19991004)38:19<2894::aid-anie2894>3.0.co;2-f

Cited by 685 publications

(566 citation statements)

References 3 publications

Supporting

Mentioning

560

Contrasting

Unclassified

Order By: Relevance

“…The performance of six different topological fingerprints and one 3D structural fingerprint was evaluated: (1) 2048-bit Daylight, 12 (2) 988-bit Unity, 13 (3) 166-bit MDL Keys, 14 (4) 1024-bit ECFP_4, 15 (5) 1024-bit FCFP_4, 15 (6) 1200-bit CATS, 16,17 and (7) FEPOPS. 18 Daylight fingerprints were generated from an in-house program based on the Daylight toolkit.…”

Section: Similarity Measuresmentioning

confidence: 99%

Quantifying the Relationships among Drug Classes

Hert

Keiser

Irwin

et al. 2008

J. Chem. Inf. Model.

153

165

View full text Add to dashboard Cite

The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacology. We calculated the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calculated with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calculate the ligand-set similarities and to the chemical representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale.

show abstract

Section: Similarity Measuresmentioning

confidence: 99%

Quantifying the Relationships among Drug Classes

Hert

Keiser

Irwin

et al. 2008

J. Chem. Inf. Model.

153

165

View full text Add to dashboard Cite

show abstract

“…15 MOE descriptors include various 2D and 3D descriptors. 2D descriptors were physical properties, subdivided surface areas, atom and bonds counts, Kier-Hall connectivity and Kappa Shape indices, adjacency and distance matrix descriptors, pharmacophore feature descriptors, and partial charges descriptors.…”

Section: Methodsmentioning

confidence: 99%

SVM-Based Feature Selection for Characterization of Focused Compound Collections

Byvatov

Schneider

2004

J. Chem. Inf. Comput. Sci.

Self Cite

View full text Add to dashboard Cite

Artificial neural networks, the support vector machine (SVM), and other machine learning methods for the classification of molecules are often considered as a "black box", since the molecular features that are most relevant for a given classifier are usually not presented in a human-interpretable form. We report on an SVM-based algorithm for the selection of relevant molecular features from a trained classifier that might be important for an understanding of ligand-receptor interactions. The original SVM approach was extended to allow for feature selection. The method was applied to characterize focused libraries of enzyme inhibitors. A comparison with classical Kolmogorov-Smirnov (KS)-based feature selection was performed. In most of the applications the SVM method showed sustained classification accuracy, thereby relying on a smaller number of molecular features than KS-based classifiers. In one case both methods produced comparable results. Limiting the calculation of descriptors to only the most relevant ones for a certain biological activity can also be used to speed up high-throughput virtual screening.

show abstract

“…Specifically, they have been inspired by the MDL 320 keys [48] and the CATS (chemically advanced template search) concept [49]: they hence combine chemical substructure recognition (MDLstyle) with topologically-relevant pharmacophore patterns based on atom-pairs (CATS-style), in an effort to bridge the gap between substructural and pharmacophore descriptors. The fingerprints are thus more general in nature than the two previous ones; they have been studied previously in an extended evaluation of descriptors for mapping chemistry-biology relationships, this validation involving over a thousand QSAR series, each containing 25 or more compounds and spanning 2 log units in activity, using automated multivariate statistics [26,50].…”

Section: Structural Representationsmentioning

confidence: 99%

Analysis and use of fragment-occurrence data in similarity-based virtual screening

Arif

Holliday

Willett

2009

J Comput Aided Mol Des

View full text Add to dashboard Cite

Current systems for similarity-based virtual screening use similarity measures in which all the fragments in a fingerprint contribute equally to the calculation of structural similarity. This paper discusses the weighting of fragments on the basis of their frequencies of occurrence in molecules. Extensive experiments with sets of active molecules from the MDL Drug Data Report and the World of Molecular Bioactivity databases, using fingerprints encoding Tripos holograms, Pipeline Pilot ECFC_4 circular substructures and Sunset Molecular keys, demonstrate clearly that frequency-based screening is generally more effective than conventional, unweighted screening. The results suggest that standardising the raw occurrence frequencies by taking the square root of the frequencies will maximise the effectiveness of virtual screening. An upper-bound analysis shows the complex interactions that can take place between representations, weighing schemes and similarity coefficients when similarity measures are computed, and provides a rationalisation of the relative performance of the various weighting schemes.

show abstract

“Scaffold-Hopping” by Topological Pharmacophore Search: A Contribution to Virtual Screening

Cited by 685 publications

References 3 publications

Quantifying the Relationships among Drug Classes

Quantifying the Relationships among Drug Classes

SVM-Based Feature Selection for Characterization of Focused Compound Collections

Analysis and use of fragment-occurrence data in similarity-based virtual screening

Contact Info

Product

Resources

About