Cheminformatic analysis

Ertl, P. Cheminformatics analysis of organic substituents identification of the most common substituents, calculation of substituent properties, and automatic identification of dmg-like bioisosteric groups. J. Chem. Inf. 

The chemical structure database and registration procedure play dual roles in an enterprise screening informatics environment. These components are essential to the transactional HTS informatics system and serve as the basis for cheminformatics data analysis. While capturing the chemical structures for association with the final screening endpoints, this system also captures and applies the organizational chemical business rules to validate and standardize chemical structures and canonicalize their representations. Structural representation is a critical prerequisite for any subsequent cheminformatics analysis. 

Hassan, M., Brown, R.D., Varma-O Brien, S., and Rogers, D. (2006) Cheminformatics analysis and learning in a data pipelining environment. Molecular Diversity, 10 (3), 283-299. 

Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A (2010) Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. ChemRes Toxicol 23, 171-83. 

Ertl P Cheminformatics Analysis of Organic Substitxients Identification of the Most Common Substituents, Calculation of Substituent Properties, and Automatic Identification of Drug-like Bioisosteric Groups. I Chem Inf Comnur Sri 2003, 43 374-380. 

Wetzel, S., Schuffenhauer, A., Roggo, S., ErtI, P. Vteildmann, H. Cheminformatic analysis of natural products and their chemical space. Chimia 61, 355-360 (2007). 

Chemoinformatics (or cheminformatics) deals with the storage, retrieval, and analysis of chemical and biological data. Specifically, it involves the development and application of software systems for the management of combinatorial chemical projects, rational design of chemical libraries, and analysis of the obtained chemical and biological data. The major research topics of chemoinformatics involve QSAR and diversity analysis. The researchers should address several important issues. First, chemical structures should be characterized by calculable molecular descriptors that provide quantitative representation of chemical structures. Second, special measures should be developed on the basis of these descriptors in order to quantify structural similarities between pairs of molecules. Finally, adequate computational methods should be established for the efficient sampling of the huge combinatorial structural space of chemical libraries. 

The life sciences domain task force (LSR DTP) has several working groups architecture and road map, biochemical pathways, cheminformatics, gene expression, sequence analysis, and single nucleotide polymorphisms. 

We have been developing cutting-edge data analysis tools for the pharmaceutical industry for over a decade, with a particular focus in the fields of cheminformatics and bioinformatics. We now describe how our insights apply to these areas. 

The four-volume Handbook of Chemoinformatics—From Data to Knowledge (Gasteiger 2003) contains a number of introductions and reviews that are relevant to chemometrics Partial Least Squares (PLS) in Cheminformatics (Eriksson et al. 2003), Inductive Learning Methods (Rose 1998), Evolutionary Algorithms and their Applications (von Homeyer 2003), Multivariate Data Analysis in Chemistry (Varmuza 2003), and Neural Networks (Zupan 2003). 

In fact, Chemoinformatics is a generic term that encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information. Related terms of chemoinformatics are cheminformatics, chemi-informatics, chemometrics, computational chemistry, chemical informatics, and chemical information management/science. 

Hundreds of books are in the marketplace about object-oriented analysis, design, and programming. A handful of books are about cheminformatics. But no book exists about how to apply object technology to the cheminformatics domain. This book is an attempt to fill that gap. 

We would like to thank Dirk Tomandl (Santhera Pharmaceuticals) for stimulating discussions, molecular framework analysis and cheminformatics support. Thanks are also due to Marcus Gastreich (BioSolvelT) for his assistance in the CDK2 virtual screening experiment and for his general advice on chemistry matters. Finally we would like to thank Christian Lemmen (BioSolvelT) for many valuable suggestions on the manuscript. 

Sarker M, Talcott C, Madrid P et al (2012) Combining cheminformatics methods and pathway analysis to identify molecules with whole cell activity against Mycobacterium tuberculosis. Pharm Res 29 2115-2127... 

The terms bioinformatics and cheminformatics refer to the use of computational methods in the study of biology and chemistry. Information from DNA or protein sequences, protein structure, and chemical structure is used to build models of biochemical systems or models of the interaction of a biochemical system with a small molecule (e.g., a drug). There are mathematical and statistical methods for analysis, public databases, and literature associated with each of these disciplines. However, there is substantial value in considering the interaction between these areas and in building computational models that integrate data from both sources. In the most... 

David Cummins is Principal Research Scientist at Eli Lilly and Company. His interests are in nonparametric regression, exploratory data analysis, simulation, predictive inference, machine learning, model selection, cheminformatics, genomics, proteomics, and metabonomics. 

Statistical HTS data analysis and discovery cheminformatics are integral components of an HTS-driven lead discovery process. Various statistical and visualization techniques are routinely... 

This chapter discusses in more detail the operational informatics systems and their integration with compound management and HTS instrumentation. It then covers various aspects of screening data analysis such as statistical methods to define hits, quality considerations, reporting, visualization, and cheminformatics-driven analysis and mining of HTS data. We start from the assumption that the HTS assay has been optimized and miniaturized on an automation platform and that the assay has been validated to deliver robust and reproducible high quality results—the prerequisite of a successful HTS campaign. 

FIGURE 14.2 (See color insert following page 114.) Components of transactional and data analysis (statistical and cheminformatics) environment in the context of HTS campaign. 

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