Michael S. Lajiness

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The problem of how to explore structure-activity relationships (SARs) systematically is still largely unsolved in medicinal chemistry. Recently, data analysis tools have been introduced to navigate activity landscapes and to assess SARs on a large scale. Initial investigations reveal a surprising heterogeneity among SARs and shed light on the relationship(More)
BACKGROUND In recent years, there has been a huge increase in the amount of publicly-available and proprietary information pertinent to drug discovery. However, there is a distinct lack of data mining tools available to harness this information, and in particular for knowledge discovery across multiple information sources. At Indiana University we have an(More)
Pharmaceutical companies are constantly racing to discover the next therapeutic blockbuster. The consensus in the industry is to focus on compounds that are by some measure drug-like, but in order to do this effectively a number of questions must be answered. For example, how should drug-like be defined and how might this definition be used to enhance drug(More)
Semantic Web Technology (SWT) makes it possible to integrate and search the large volume of life science datasets in the public domain, as demonstrated by well-known linked data projects such as LODD, Bio2RDF, and Chem2Bio2RDF. Integration of these sets creates large networks of information. We have previously described a tool called WENDI for aggregating(More)
Systems chemical biology, the integration of chemistry, biology and computation to generate understanding about the way small molecules affect biological systems as a whole, as well as related fields such as chemogenomics, are central to emerging new paradigms of drug discovery such as drug repurposing and personalized medicine. Recent Semantic Web(More)
Binary quantitative structure-activity relationship (QSAR) is an approach for the analysis of high throughput screening (HTS) data by correlating structural properties of compounds with a "binary" expression of biological activity (1 = active and 0 = inactive) and calculating a probability distribution for active and inactive compounds in a training set.(More)
The postantibiotic effect (PAE) is a suppression of bacterial growth that persists after a short exposure to antimicrobials. The active antibiotic delays the resumption of normal growth. This suppression of bacterial growth following antibiotic removal is described by a two phase model. The quantification of PAE is a function of the model parameters, for(More)
Recent years have seen a huge increase in the amount of publicly-available information relevant to drug discovery, including online databases of compound and bioassay information; scholarly publications linking compounds with genes, targets and diseases; and predictive models that can suggest new links between compounds, genes, targets and diseases.(More)
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