Christos A. Nicolaou

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Hierarchical clustering algorithms such as Wards or complete-link are commonly used in compound selection and diversity analysis. Many such applications utilize binary representations of chemical structures, such as MACCS keys or Daylight fingerprints, and dissimilarity measures, such as the Euclidean or the Soergel measure. However, hierarchical clustering(More)
Improving the profile of a molecule for the drug-discovery process requires the simultaneous optimization of numerous, often competing objectives. Traditionally, standard chemoinformatics methods ignored this problem and focused on the sequential optimization of each single biological or chemical property (ie, a single objective). This approach, known as(More)
A simple yet powerful programming tool enabling in silico experimentation, end-to-end data management through web services as well as use of grid and cloud processing power is scientific workflows. This technology is receiving considerable interest in recent years primarily due to its ability to promote and support scientific collaboration among large(More)
Computer-aided drug discovery techniques have been widely used in recent years to support the development of new pharmaceuticals. Virtual screening, the computational counterpart of experimental screening, attempts to replicate the results from in vitro and in vivo methods through the use of in silico models and tools. This paper presents the LISIs(More)
As the use of high-throughput screening systems becomes more routine in the drug discovery process, there is an increasing need for fast and reliable analysis of the massive amounts of the resulting data. At the forefront of the methods used is data reduction, often assisted by cluster analysis. Activity thresholds reduce the data set under investigation to(More)
Drug discovery and development is a complex, lengthy process, and failure of a candidate molecule can occur as a result of a combination of reasons, such as poor pharmacokinetics, lack of efficacy, or toxicity. Successful drug candidates necessarily represent a compromise between the numerous, sometimes competing objectives so that the benefits to patients(More)
Drug discovery is a challenging multi-objective problem where numerous pharmaceutically important objectives need to be adequately satisfied for a solution to be found. The problem is characterized by vast, complex solution spaces further perplexed by the presence of conflicting objectives. Multi-objective optimization methods, designed specifically to(More)
Substructure mining is a well-established technique used frequently in drug discovery. Its aim is to discover and characterize interesting 2D substructures present in chemical datasets. The popularity of the approach owes a lot to the success of the structure-activity relationship practice, which states that biological properties of molecules are a result(More)
Advancements in combinatorial chemistry and high-throughput screening technology have enabled the synthesis and screening of large molecular libraries for the purposes of drug discovery. Contrary to initial expectations, the increase in screening library size, typically combined with an emphasis on compound structural diversity, did not result in a(More)
Could high-quality in silico predictions in drug discovery eventually replace part or most of experimental testing? To evaluate the agreement of selectivity data from different experimental or predictive sources, we introduce the new metric concordance minimum significant ratio (cMSR). Empowered by cMSR, we find the overall level of agreement between(More)