We exploit the recent availability of a community reconstruction of the human metabolic network (‘Recon2’) to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) ‘nearest’ human metabolite. This suggests a ‘rule of 0.5’ mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This ‘rule of 0.5’ may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.

/pdf/a-rule-of-0-5-for-the-metabolite-likeness-of-approved-4er46hyyck.pdf

A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs.

Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.

The impact of chemoinformatics on drug discovery in the pharmaceutical industry.

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used therapeutic agents that exhibit frequent and sometimes severe adverse effects, including gastrointestinal ulcerations and cardiovascular disorders. In an effort to obtain safer NSAIDs, we assessed the direct cyclooxygenase (COX) inhibition activity and we investigated the potential COX binding mode of some previously reported 2-(trimethoxyphenyl)-thiazoles. The in vitro COX inhibition assays were performed against ovine COX-1 and human recombinant COX-2. Molecular docking studies were performed to explain the possible interactions between the inhibitors and both COX isoforms binding pockets. Four of the tested compounds proved to be good inhibitors of both COX isoforms, but only compound A3 showed a good COX-2 selectivity index, similar to meloxicam. The plausible binding mode of compound A3 revealed hydrogen bond interactions with binding site key residues including Arg120, Tyr355, Ser530, Met522 and Trp387, whereas hydrophobic contacts were detected with Leu352, Val349, Leu359, Phe518, Gly526, and Ala527. Computationally predicted pharmacokinetic profile revealed A3 as lead candidate. The present data prove that the investigated compounds inhibit COX and thus confirm the previously reported in vivo anti-inflammatory screening results suggesting that A3 is a suitable candidate for further development as a NSAID.

COX Inhibition Profile and Molecular Docking Studies of Some 2-(Trimethoxyphenyl)-Thiazoles.

In recent past several efforts have been made to analyze the symptoms, causes, and cure of Alzheimer’s disease (AD) and also to reveal biochemical changes and pathogenesis of the brain affected with AD. Several studies indicated the main cause of the disease is deposition of necrotic β-amyloid plaques in the brain. The enzymes β-secretase and γ-secretase catalyze the β-amyloid production. It has also been observed that the cells producing acetylcholine, a major neurotransmitter, are destroyed by two closely related enzymes namely acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) in AD progression leading to cognitive disabilities. Hence, the research is going on in finding the inhibitors for these enzymes which will help to either prolong or cure the AD. Discovering the inhibitors for AChE and BChE without side effects remains a major challenge as the AChE and BChE inhibiting drugs available possess several side effects. Several new drugs are being discovered utilizing medicinal plant resources. Uses of flavonoids as plant secondary metabolites are being tried for the treatment of AD. Efforts are being made to apply computational knowledge to streamline the drug discovery process. Nevertheless, blood-brain barrier (BBB) permeation plays a vital role in drug discovery. BBB a physical barrier in the brain through which the central nervous system therapeutic molecule has to permeate for its activity. Finding the potent lead candidates capable of crossing the BBB remains to be a major challenge in neurodegenerative diseases. In the present review, attempts have been made to discuss on all these important aspects. Keywords: Alzheimer’s disease, Amyloid precursor protein, β-amyloid, Blood-brain barrier, Flavonoids, Molecular docking, Multi-enzyme targeting.

Alzheimer’s and current therapeutics: a review

Material informatics is engaged with the application of informatic principles to materials science in order to assist in the discovery and development of new materials. Central to the field is the application of data mining techniques and in particular machine learning approaches, often referred to as Quantitative Structure Activity Relationship (QSAR) modeling, to derive predictive models for a variety of materials-related "activities". Such models can accelerate the development of new materials with favorable properties and provide insight into the factors governing these properties. Here we provide a comparison between medicinal chemistry/drug design and materials-related QSAR modeling and highlight the importance of developing new, materials-specific descriptors. We survey some of the most recent QSAR models developed in materials science with focus on energetic materials and on solar cells. Finally we present new examples of material-informatic analyses of solar cells libraries produced from metal oxides using combinatorial material synthesis. Different analyses lead to interesting physical insights as well as to the design of new cells with potentially improved photovoltaic parameters.

Materials Informatics: Statistical Modeling in Material Science.

Thank you for downloading chemoinformatics in drug discovery. Maybe you have knowledge that, people have search numerous times for their favorite readings like this chemoinformatics in drug discovery, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful bugs inside their laptop. chemoinformatics in drug discovery is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the chemoinformatics in drug discovery is universally compatible with any devices to read.

Paul Bã ¼ rger

Papers

Chemoinformatics In Drug Discovery