Virtual similarity-based

Willet P. Similarity-based approaches to virtual screening. Biochem Soc Trans 2003 31 603-6. 

Molecular similarity (similarity-based virtual screening) on the basis of one or a small set of known actives, molecules showing a high similarity concerning specific features stored in a molecular descriptor are searched for. 

HertJ, Willett P, Wilton D, etal. (2004) Topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. Org. Biomol. Chem. 2 3256-3266. 

Enhancing the Effectiveness of Similarity-Based Virtual Screening Using Nearest-Neighbour Information. /. Med. Chem. 48 7049-7054. 

The group of Prof Peter Willett from the University of Sheffield summarizes in Chapter 6 new chemoinformatics methods for similarity-based virtual screening which based on known active compounds are useful for the identification of new ligands for targets related by conserved molecular recognition. 

Willett, P. (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11, 1046-1053. 

In a similar method, computer models can also be developed to screen virtual hbraries based on the binding pockets of receptors.34 In this case, the computational models directly probe the binding sites in receptors... 

One more way to conduct a similarity-based virtual screening is to retrieve the structures containing a user-defined set of pharmacophoric features. In the Dynamic Mapping of Consensus positions (DMC) algorithm those features are selected by finding common positions in bit strings for all active compounds. The potency-scaled DMC algorithm (POT-DMC) " is a modification of DMC in which compounds activities are taken into account. The latter two methods may be considered as intermediate between conventional similarity search and probabilistic SAR approaches. 

Simplistic and heuristic similarity-based approaches can hardly produce as good predictive models as modern statistical and machine learning methods that are able to assess quantitatively biological or physicochemical properties. QSAR-based virtual screening consists of direct assessment of activity values (numerical or binary) of all compounds in the database followed by selection of hits possessing desirable activity. Mathematical methods used for models preparation can be subdivided into classification and regression approaches. The former decide whether a given compound is active, whereas the latter numerically evaluate the activity values. Classification approaches that assess probability of decisions are called probabilistic. 

Prior to a brief discussion of specific applications of topological pharmacophore fingerprints in similarity-based virtual screening, some general remarks can be made. 

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