Computational Approaches in Cheminformatics and Bioinformatics


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In most cases in vitro measurements are carried out using different cell models, human or animal. For measuring permeability, and thus absorption, the use of artificial membranes, called PAMPA parallel artificial membrane permeability assay has become a popular alternative to the CACO-2 human colon carcinoma cell line van de Waterbeemd et al. PAMPA is, however, only useful for measuring passive permeability.

For in vitro studies of liver toxicity, the rat hepatoma cell line is a well characterized and evaluated method, which is often followed up by human hepatoma cell line studies van de Waterbeemd et al.

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Toxicogenomic studies using microarray expression techniques have also become increasingly important. Major concerns in toxicological studies are multiple endpoints, dose—response relationships and selection of endpoints. Other important aspects are purity of drugs, protein binding and metabolic stability among others. Likewise, various computational methods are evolving rapidly at present.


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Computational techniques used to search through chemical libraries and databases, so-called virtual screening methods, have become increasingly popular in drug discovery Walters et al. A whole range of computational techniques are used for searching for molecular similarities and dissimilarities Sello, ; Willett, ; Bajorath, ; Gillet et al.

Chemoinformatics is strongly linked to computational chemistry and molecular modeling Burkert and Allinger, ; Jensen, ; Cramer, Molecular modeling methods are particularly useful for conducting conformational analysis of molecules, and for accessing the strength of intermolecular interactions. Newly established fields like chemogenomics or chemical genomics , metabonomics and metabolomics also play increasingly important roles in modern drug discovery and development.

Chemogenomics Browne et al. In metabonomics Nicholson and Wilson, relatively low-molecular weight materials produced during genomic expression within a cell are studied, normally by use of 1 H-NMR spectroscopy and multivariate data analysis chemometrics Geladi and Kowalski b , a. It has been shown to be a useful tool for understanding drug efficacy and toxicity.

Metabolomics is similar to metabonomics, but where metabonomics deals with integrated, multicellular, biological systems, metabolomics deals with simple cell systems. A brief overview of various methods for predicting ADMET adsorption, distribution, metabolism, excretion and toxicity properties, BBB penetration and key physico-chemical properties is also given, but it must be stressed that this overview is by no means complete.

References are given to more detailed review papers for the various properties. The fundamental behavior of substances is governed by intermolecular interactions at various levels.

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Physico-chemical properties of drug molecules are mostly governed by the interactions between the drug molecules and the surrounding aqueous environment. The potency of drugs depends on how well a given drug molecule ligand fits into a target, and how strong the interactions between the ligand and the target are, often studied with computational methods, e.

ADMET properties depend on how the drug molecule interacts with a large number of macromolecules in the human organism. In cases where a patient uses more than one drug, drug—drug interactions are also of importance, and such interactions are unfortunately often ignored. The human organism is an immensely complicated system, but modeling of properties can be accomplished by studying various subsystems and their interactions using a broad range of computational and experimental methods.

Recently, Macchiarulo et al.

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Based on their results they propose that HTS should not only involve a selection of small molecules, but also a panel of proteins to test for cross-reactivity. The availability of reliable experimental data and basic structural information is crucial for successful modeling work.


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In this section various databases relevant to drug discovery and development are discussed, including databases for available organic compounds, screening compounds, medicinal agents drugs , as well as databases with ADMET properties and physico-chemical properties. A few protein databases are also mentioned. An overview of the databases and key features is given in Table 1 , and the type of properties provided is listed in Table 2.

Most of the databases provide 1-dimensional 1D , 2-dimensional 2D and 3-dimensional 3D structural information see below for further detail on molecular descriptors. In cases where only 1D and 2D information is given, several programs for generating 3D structures from 2D structures are available.

One should, however, bear in mind that due to conformational flexibility many 3D structures equilibrium conformations may exist for each molecule. Examples of 1D, 2D and 3D structures are shown in Figure 2. The best known database for synthetic organic and inorganic compounds is the Available Chemicals Directory ACD , 1 which currently includes approximately , unique substances. ACD is often used for representing non-drugs in model development, and although some compounds within ACD may be biologically active, the majority of compounds are not. The physical property information is, however, not well organized within the Spresi database.

A number of databases have been developed especially for screening purposes. For log P , experimental and calculated records are provided, and for p K a measured records are given. The MDDR contains , drugs launched and under development, collected from the patent literature and other relevant sources. MDDR is updated monthly, which adds up to approximately 10, new compounds per year, and includes information about drug class and drug activity in qualitative terms as well.

WDI contains around 73, marketed drugs and drugs under development, with each record classified by drug activity, mechanism of action, treatment, among other factors. The Derwent Drug File is a highly focused database of selected journal articles and conference reports on all aspects of drug development. There are a number of other available drug databases. The MedChem database consists of 48, compounds, 67, measured log P values, 13, measured p K a values and 19, pharmacological drug activities.

According to the providers of the MedChem Bio-loom database, all available data for log P and p K a have been gathered from the literature. In addition, the database includes a sorted list containing only those measurements carried out with high quality methods. The MedChem database also contains a collection of biological and physico-chemical data intended for QSPR quantitative structure—property relationship modeling, and software for calculating log P values Clog P.

The biological activities in this database are mainly related to receptor antagonists and enzyme inhibitors, which are ranked by target class. The BIOSTER is a database of bioanalogous pairs of molecules bio-isosters , and contains over active molecules, including drugs, agrochemicals and enzyme inhibitors. The MDL Metabolite database contains information about metabolism pathways for xenobiotic compounds and biotransformations primary medicinal agents and experimental data from in vivo and in vitro studies.

The database includes more than 10, parent compounds, over 64, biotransformations and over 40, molecules parent compounds, intermediates and final metabolites. Metabolism data are also found in the Accelrys' Metabolism and Biotransformation databases.

The Metabolism database covers vertebrates animals , invertebrates and plants. This databases contains parent compounds, 30, transformations and is being extended to 40, transformations. The Accelrys' Biotransformation database, on the other hand, is a stand-alone database for vertebrates only, containing unique parent compounds, and transformations. The database contains six categories of data, i. It also contains detailed information about how the in vivo and in vitro experiments were carried out, including species of organism or tissue studied, dose, etc.

The ToxExpress Reference Database is a toxicogenomic database, which contains gene expression profiles from known toxicants. This database is built on in vivo and in vitro studies of exposure to toxicants, and around compounds have been profiled. There are other important databases for small molecules. CrossFire Beilstein contains more than 8 million organic compounds, over 9 million reactions, a variety of properties, including various physical properties, pharmacodynamics and environmental toxicity.

This database contains over , bioactive compounds. In this context, we would also like to mention a few relevant macromolecular and crystallographic databases with drug-discovery relevant information. The Protein Data Bank PDB is the main source for available protein crystal structures and structural information obtained with NMR spectroscopy, with currently more than 28, structures, and a weekly growth of about structures.

The Cambridge Structural Database CSD is the most comprehensive collection of crystallographic data for small molecules, containing around , structures. Although CSD has a much larger number of entries, the relative growth rate within PDB is higher, which shows clearly the increased focus on protein structure determination world wide structural genomics. Relibase Hendlich et al.

Ji et al. A number of databases containing protein—protein interaction data are available, including Database of Interacting Proteins DIP Xenearios et al. Protein—protein interaction databases are collections of information about actual protein—protein contacts or complex associations within the proteome of specific organisms, in particularly in yeast, but also in humans, fruitfly and other organisms.

Computational Approaches in Cheminformatics and Bioinformatics Computational Approaches in Cheminformatics and Bioinformatics
Computational Approaches in Cheminformatics and Bioinformatics Computational Approaches in Cheminformatics and Bioinformatics
Computational Approaches in Cheminformatics and Bioinformatics Computational Approaches in Cheminformatics and Bioinformatics
Computational Approaches in Cheminformatics and Bioinformatics Computational Approaches in Cheminformatics and Bioinformatics
Computational Approaches in Cheminformatics and Bioinformatics Computational Approaches in Cheminformatics and Bioinformatics
Computational Approaches in Cheminformatics and Bioinformatics Computational Approaches in Cheminformatics and Bioinformatics
Computational Approaches in Cheminformatics and Bioinformatics Computational Approaches in Cheminformatics and Bioinformatics

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