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Fragment-based lead generation has led to the discovery of a novel series of cyclic amidine-based inhibitors of beta-secretase (BACE-1). Initial fragment hits with an isocytosine core having millimolar potency were identified via NMR affinity screening. Structure-guided evolution of these fragments using X-ray crystallography together with potency determination using surface plasmon resonance and functional enzyme inhibition assays afforded micromolar inhibitors. Similarity searching around the isocytosine core led to the identification of a related series of inhibitors, the dihydroisocytosines. By leveraging the knowledge of the ligand-BACE-1 recognition features generated from the isocytosines, the dihydroisocytosines were efficiently optimized to submicromolar potency. Compound 29, with an IC50 of 80 nM, a ligand efficiency of 0.37, and cellular activity of 470 nM, emerged as the lead structure for future optimization.

Fragment-based lead discovery has been successfully applied to the aspartyl protease enzyme beta-secretase (BACE-1). Fragment hits that contained an aminopyridine motif binding to the two catalytic aspartic acid residues in the active site of the enzyme were the chemical starting points. Structure-based design approaches have led to identification of low micromolar lead compounds that retain these interactions and additionally occupy adjacent hydrophobic pockets of the active site. These leads form two subseries, for which compounds 4 (IC50 = 25 microM) and 6c (IC50 = 24 microM) are representative. In the latter series, further optimization has led to 8a (IC50 = 690 nM).

Abstract:Remote sensing continues to be an invaluable tool in earthquake damage assessments and emergency response. This study evaluates the effectiveness of multilayer feedforward neural networks, radial basis neural networks, and Random Forests in detecting earthquake damage caused by the 2010 Port-au-Prince, Haiti 7.0 moment magnitude (M w ) event. Additionally, textural and structural features including entropy, dissimilarity, Laplacian of Gaussian, and rectangular fit are investigated as key variables for high spatial resolution imagery classification. Our findings show that each of the algorithms achieved nearly a 90% kernel density match using the United Nations Operational Satellite Applications Programme (UNITAR/UNOSAT) dataset as validation. The multilayer feedforward network was able to achieve an error rate below 40% in detecting damaged buildings. Spatial features of texture and structure were far more important in algorithmic classification than spectral information, highlighting the potential for future implementation of machine learning algorithms which use panchromatic or pansharpened imagery alone.

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