Random compound selection

The general aim of VS methods is to retrieve from a molecular database a fraction of true positives that is significantly larger than that of a random compound selection. If a VS method selects n molecules from a database with N entries, the selected hit list consists of active compounds (true positive compounds, TP) and decoys (false positive compounds, FP). Active molecules that are not retrieved by the VS method are defined false negatives FN), whereas the unselected database decoys represent the true negatives TN) (Figure 3.3). 

One of the most popular descriptors for evaluating VS methods is the yield of actives (Equation 3.1). This descriptor quantifies the probability that one of n selected compounds is active. In other words, it represents the hit rate if all compounds selected by the VS protocol are tested for activity.However, it contains no information about the consistence of the database and the increase of the ratio of active molecules to inactives within a VS compound selection compared to a random compound selection ... 

The maximum dissimilarity algorithm works in an iterative manner at each step one compormd is selected from the database and added to the subset [Kennard and Stone 1969]. The compound selected is chosen to be the one most dissimilar to the current subset. There are many variants on this basic algorithm which differ in the way in which the first compound is chosen and how the dissimilarity is measured. Three possible choices for fhe initial compormd are (a) select it at random, (b) choose the molecule which is most representative (e.g. has the largest sum of similarities to the other molecules) or (c) choose the molecule which is most dissimilar (e.g. has the smallest sum of similarities to the other molecules). 

The mechanisms of photoreactivity of coordination compounds limiting cases of decay on a specific nuclear coordinate (dosenco) or via random coordinate selection (dercos). B. R. Hollebone, C. H. Langford and N. Serpone, Coord. Chem. Rev., 1981, 39,181-224 (95). 

Dissimilarity-based compound selection (DECS) methods involve selecting a subset of compounds directly based on pairwise dissimilarities [37]. The first compound is selected, either at random or as the one that is most dissimilar to all others in the database, and is placed in the subset. The subset is then built up stepwise by selecting one compound at a time until it is of the required size. In each iteration, the next compound to be selected is the one that is most dissimilar to those already in the subset, with the dissimilarity normally being computed by the MaxMin approach [38]. Here, each database compound is compared with each compound in the subset and its nearest neighbor is identified the database compound that is selected is the one that has the maximum dissimilarity to its nearest neighbor in the subset. 

Preprocessing both the vendor and Pharmacia compound collections has resulted in a substantial improvement in the quality of the compounds selected and in the performance of vendors in supplying the compounds on a timely basis. In 2002, more than 83% of the nearly 60 000 compounds ordered were obtained, exceeding our earlier performances that were usually in the neighborhood of 50%. This is partially due to the preselection of vendors who had the ability to provide an appropriate amount ( 50 mg) of sample. Unfortunately, we are unable to directly compare our approach with those tried by others or with an entirely random sampling approach. Nevertheless, it is clear that the compoimd acquisition process described here does, indeed, provide an effective means for enriching our corporate compound collection. 

There is a possibility that an FC state will react before complete thermal equilibration. In the case of diatomic molecules, the process is usually known as predissociation — a dissociative state crosses the excited state potential surface. The situation is more complicated in the case of a coordination compound, but one can imagine an FC state relaxing along some nuclear coordinate leading to bond breaking. A state capable of such a process has been called a DOSENCO state, an acronym for Decay On SElected Nuclear Coordinates .21 The same authors use the term DERCOS (DEcay via Random Coordinate Selection) for a thexi state. 

Dissimilarity analysis plays a major role in compound selection. Typical tasks include the selection of a maximally dissimilar subset of compounds from a large set or the identification of compounds that are dissimilar to an existing collection. Such issues have played a major role in compound acquisition in the pharmaceutical industry. A typical task would be to select a subset of maximally dissimilar compounds from a data set containing n molecules. This represents a non-trivial challenge because of the combinatorial problem involved in exploring all possible subsets. Therefore, other dissimilarity-based selection algorithms have been developed (Lajiness 1997). The basic idea of such approaches is to initially select a seed compound (either randomly or, better, based on dissimilarity to others), then calculate dissimilarity between the seed compound and all others and select the most dissimilar one. In the next step, the database compound most dissimilar to these two compounds is selected and added to the subset, and the process is repeated until a subset of desired size is obtained. 

Potter and Matter [64] compared maximum dissimilarity methods and hierarchical clustering with random methods for designing compound subsets. The compound selection methods were applied to a database of 1283 compounds extracted from the IndexChemicus 1993 database that contain 55 biological activity classes. A second database consisted of 334 compounds from 11 different QSAR target series. They compared the distribution of actives in randomly chosen subsets with the rationally... 

Selecting 24 random compounds (none from tables of related structures) from different sections of the authors review [12] on long-range heteronuclear... 

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