Strategic Considerations in the Design of a Screening System for Substructure Searches of Chemical Structure Files

Adamson, G. W.; Cowell, Jeanne; Lynch, Michael; McLure, Alice H.W.; Town, William G.; Yapp, A. M.

doi:10.1021/c160050a013

Cited by 70 publications

(45 citation statements)

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“…1). It is similar to augmented atoms 12 or the extended connectivity circular fingerprints (ECFPs) employed by Scitegic (San Diego, CA). 13 Feature selection is performed using information-gain-based feature selection, 13 which was originally devised to induce rules as nodes of decision trees.…”

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confidence: 99%

Screening for Dihydrofolate Reductase Inhibitors Using MOLPRINT 2D, a Fast Fragment-Based Method Employing the Naïve Bayesian Classifier: Limitations of the Descriptor and the Importance of Balanced Chemistry in Training and Test Sets

Bender¹,

Mussa²,

Glen³

2005

SLAS Discovery

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A fragment-based similarity searching method, MOLPRINT 2D, was employed for virtual screening of Escherichia coli dihydrofolate reductase inhibitors. Using the original training set of 50,000 compounds, only marginal enrichment factors (between 1 and 3) could be achieved on the test library. The active structures contained in the training and test libraries represented different types of "chemistry," that is, different substructural features associated with activity. Training and test sets were pooled in a 2nd step and randomly split into training and test of equal size, with the objective of smoothing out the different chemical characteristics of both libraries. In a 10-fold cross-validation study on the new training and test sets, typically 10-fold enrichment could be found in the first 96 positions, 4-fold enrichment in the first 384 positions, and 3-fold enrichment in the first 1536 positions, corresponding to 6, 10, and 28 hits, respectively (out of a total of 307; activity defined as average residual activity of less than 80%). The conclusions are 2-fold. On one hand, the exact fragment-matching similarity searching method employed here is not capable of finding completely novel hit structures. On the other hand, this study emphasizes the requirement for a comparable distribution of chemical features of the training and test sets. MOLPRINT 2D is freely downloadable from

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mentioning

confidence: 99%

Screening for Dihydrofolate Reductase Inhibitors Using MOLPRINT 2D, a Fast Fragment-Based Method Employing the Naïve Bayesian Classifier: Limitations of the Descriptor and the Importance of Balanced Chemistry in Training and Test Sets

Bender¹,

Mussa²,

Glen³

2005

SLAS Discovery

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“…This important ligand-based drug discovery methodology and classification approach are associated with the following two fundamental computational problems. (1) The notion of similarity used in search determines the molecules that are extracted from the database. A notion of similarity which has the highest level of bioactivity discrimination is very desirable and needs to be determined computationally.…”

Section: Introductionmentioning

confidence: 99%

Novel approaches for small biomolecule classification and structural similarity search

Karakoç¹,

Cherkasov²,

Şahinalp³

2007

SIGKDD Explor. Newsl.

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Structural similarity search among small molecules is a standard tool used in molecular classification and in-silico drug discovery. The effectiveness of this general approach depends on how well the following problems are addressed. The notion of similarity should be chosen for providing the highest level of discrimination of compounds with respect to the bioactivity of interest. The data structure for performing search should be very efficient as the molecular databases of interest include several millions of compounds. In this paper we summarize the recent applications of knearest-neighbor search method for small molecule classification. The k-nn classification of small molecules is based on selecting the most relevant set of chemical descriptors which are then compared under standard Minkowski distance Lp. Here we describe how to computationally design the optimal weighted Minkowski distance wLp for maximizing the discrimination between active and inactive compounds wrt bioactivities of interest. k-nn classification requires fast similarity search for predicting bioactivity of a new molecule. We then focus on construction of pruning based k-nn search data structures for any wLp distance that minimizes similarity search time. The accuracy achieved by k-nn classifier is better than the alternative LDA and MLR approaches and is comparable to the ANN methods. In terms of running time, k-nn classifier is considerably faster than the ANN approach especially when large data sets are used. Furthermore, k-nn classifier is capable of quantification of the level of bioactivity rather than returning a binary decision and can bring more insight to the nature of the activity via eliminating unrelated descriptors of the compounds with respect to the activity in question.

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“…Towards this goal, a number of different approaches have been developed that represent each compound by a set of descriptors that are based on frequency, physiochemical properties as well as topological and geometric substructures (fragments) [1,3,6,8,13,[28][29][30]36]. Historically, the best performing and most widely used descriptors have been based on fingerprints, which represent each molecular graph by a fixed length bit-vector derived by enumerating all bounded length cycles and paths in the graph (e.g., Daylight [29]), and on sets of fragments that have been identified a priori by domain experts (e.g., Maccs keys [30]).…”

Section: Introductionmentioning

confidence: 99%

Acyclic Subgraph Based Descriptor Spaces for Chemical Compound Retrieval and Classification

Wale

Karypis²

2006

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In recent years the development of computational techniques that build models to correctly assign chemical compounds to various classes or to retrieve potential drug-like compounds has been an active area of research. These techniques are used extensively at various phases during the drug development process. Many of the best-performing techniques for these tasks, utilize a descriptor-based representation of the compound that captures various aspects of the underlying molecular graph's topology. In this paper we introduce and describe algorithms for efficiently generating a new set of descriptors that are derived from all connected acyclic fragments present in the molecular graphs. In addition, we introduce an extension to existing vector-based kernel functions to take into account the length of the fragments present in the descriptors. We experimentally evaluate the performance of the new descriptors in the context of SVM-based classification and ranked-retrieval on 28 classification and retrieval problems derived from 17 datasets. Our experiments show that for both the classification and retrieval tasks, these new descriptors consistently and statistically outperform previously developed schemes based on the widely used fingerprint-and Maccs keys-based descriptors, as well as recently introduced descriptors obtained by mining and analyzing the structure of the molecular graphs.

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Strategic Considerations in the Design of a Screening System for Substructure Searches of Chemical Structure Files

Cited by 70 publications

References 0 publications

Screening for Dihydrofolate Reductase Inhibitors Using MOLPRINT 2D, a Fast Fragment-Based Method Employing the Naïve Bayesian Classifier: Limitations of the Descriptor and the Importance of Balanced Chemistry in Training and Test Sets

Screening for Dihydrofolate Reductase Inhibitors Using MOLPRINT 2D, a Fast Fragment-Based Method Employing the Naïve Bayesian Classifier: Limitations of the Descriptor and the Importance of Balanced Chemistry in Training and Test Sets

Novel approaches for small biomolecule classification and structural similarity search

Acyclic Subgraph Based Descriptor Spaces for Chemical Compound Retrieval and Classification

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