Database mining

Many reports have been published that address various aspects of diversity analysis in the context of chemical library design and database mining [77-84]. 

Shen M, Beguin C, Golbraikh A, Stables JP, Kohn H, Tropsha A. Application of predictive QSAR models to database mining identification and experimental validation of novel anticonvulsant compounds. J Med Chem 2004 47(9) 2356-64. 

The are several clearance and toxicological aspects that have to be considered in the drug discovery process such as metabolic stability, enzyme selectivity, CYP inhibition and type of inhibition. Among these factors, the prediction of the site of metabolism has become one of the most successful parameters for prediction. The knowledge of the site of metabolism enhances the opportunity to chemically modify the molecule to improve the metabolic stability. There are several approaches based on database mining, chemical reactivity, protein interaction or both that have been developed for the prediction of this property, with different degree of success and applicability. 

Cruciani, G., Pastor, M., and Mannhold, R. Suitability of molecular descriptors for database mining. A comparative analysis./. Med. Chem. 

Tropsha, a. Application of predictive QSAR models to database mining. 

Application of Predictive QSAR Models to Database Mining... 

The advantage of using QSAR models for database mining is that it affords not only the compounds selection but also quantitative prediction of their activity. For illustration, we shall discuss our recent success in developing validated predictive models of anticonvulsants [51] and their application to the discovery of novel potent compounds by the means of database mining [10]. 

Fig. 16.3 Flowchart of database mining using QSAR models.

Fig. 16.5 Computer-aided drug discovery workflow based on combination of QSAR modeling and consensus database mining as applied to the discovery of novel anticonvulsants [10]. The workflow emphasizes the importance of model validation and applicability domain in ensuring high hit rates as a result of database mining with predictive QSAR models.

Teopsha, a., Cho, S.)., Zheng, W. New Tricks for an Old Dog development and application of novel QSAR methods for rational design of combinatorial chemical libraries and database mining. In Rational Drug Design Novel Methodology and Practical Applications, ACS Symposium Series Vol. 719, Paeeill,... 

Teopsha, A. Zheng, W. Identification of the descriptor pharmacophores using variable selection QSAR applications to database mining. Curr. Pharm. Des. 2001, 7, 599-612. 

Once the process criteria have been identified various strategies can be followed to obtain the biocatalyst for the desired biotiansformation. Most commonly, first a literature, patent and electronic media search is performed ( database mining ) in order to find established biocatalysts that are known to catalyze the desired reaction or that catalyze a reaction that is similar to it (see 5.3.2). Databases that are becoming... 

Medina-Franco, J. L., Golbraikh, A., Oloff, S., Castillo, R., Tropsha, A. (2005) Quantitative structure-activity relationship analysis of pyridinone HIV-1 reverse transcriptase inhibitors using the nearest neighbor method and QSAR-based database mining. J Comput Aided Mol Des 19, 229-242. 

Zhang, S., Wei, L., Bastow, K., Zheng, W., Brossi, A., Lee, . H., Tropsha, A. (2007) Antitumor Agents 252. Application of validated QSAR models to database mining discovery of novel tylophorine derivatives as potential anticancer agents. J Comput Aided Mol Des 21, 97-112. 

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