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Transforming growth factor-β (TGF-β) signalling plays a key role in colorectal cancer (CRC). Bone morphogenetic protein-4 (BMP4) is a member of the TGF-β family of signal transduction molecules. To examine if germline mutation in BMP4 causes CRC we analysed 504 genetically enriched CRC cases (by virtue of early-onset disease, family history of CRC) for(More)
Optimization of the imidazo[4,5-b]pyridine-based series of Aurora kinase inhibitors led to the identification of 6-chloro-7-(4-(4-chlorobenzyl)piperazin-1-yl)-2-(1,3-dimethyl-1H-pyrazol-4-yl)-3H-imidazo[4,5-b]pyridine (27e), a potent inhibitor of Aurora kinases (Aurora-A K(d) = 7.5 nM, Aurora-B K(d) = 48 nM), FLT3 kinase (K(d) = 6.2 nM), and FLT3 mutants(More)
In this paper we propose a novel graph-based genetic algorithm for the evolution of novel molecular graphs from a predefined set of elements or molecular fragments with an external objective function. A brief overview of existing genetic algorithm approaches in molecular design is provided followed by a description of our approach. The paper continues to(More)
Following the theoretical model by Hann et al. moderately complex structures are preferable lead compounds since they lead to specific binding events involving the complete ligand molecule. To make this concept usable in practice for library design, we studied several complexity measures on the biological activity of ligand molecules. We applied the(More)
The ever-growing malware threat in the cyber space calls for techniques that are more effective than widely deployed signature-based detection systems and more scalable than manual reverse engineering by forensic experts. To counter large volumes of malware variants, machine learning techniques have been applied recently for automated malware(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)
The scaffold diversity of 7 representative commercial and proprietary compound libraries is explored for the first time using both Murcko frameworks and Scaffold Trees. We show that Level 1 of the Scaffold Tree is useful for the characterization of scaffold diversity in compound libraries and offers advantages over the use of Murcko frameworks. This(More)
An evolutionary statistical learning method was applied to classify drugs according to their biological target and also to discriminate between a compilation of oral and nonoral drugs. The emphasis was placed not only on how well the models predict but also on their interpretability. In an enhancement to previous studies, the consistency of the model(More)
A workflow for the inverse quantitative structure-property relationship (QSPR) problem is reported in this paper for the de novo design of novel chemical entities (NCE) in silico through the application of existing QSPR models to calculate multiple objectives, including prediction confidence measures, to be optimized during the de novo design process. Two(More)