Multivariate chemical mapping of pigments and binders in easel painting cross-sections by micro IR reflection spectroscopy.

Paintings are composed of superimposed layers of inorganic and organic materials (pigments and binders). Knowledge of the stratigraphic sequence of these heterogeneous layers is fundamental for understanding the artist's painting technique and for conservation issues. In this study, micro-IR mapping experiments in reflection mode have been carried out on cross-sections taken from simulations of ancient easel paintings. The objective was to locate both organic binders and inorganic pigments. Chemical maps have been re-constructed using the common approach based on the integration of specific infrared bands. However, owing to the complexity of painting materials, this approach is not always applicable when dealing with broad and superimposed spectral features and with reststrahlen or derivative-like bands resulting from acquisition in reflection mode. To overcome these limitations, principal-component analysis has been successfully used for the re-construction of the image, extracting the relevant information from the complex full spectral data sets and obtaining reliable chemical distributions of the stratigraphy materials. Different pigment-binder combinations have been evaluated in order to understand limitations and strengths of the approach. Finally, the method has been applied for stratigraphic characterization of a cross-section from a 17th century wooden sculpture identifying both the original paint layer and the several overpaintings constituting the complex stratigraphy.

FLAP: GRID molecular interaction fields in virtual screening. validation using the DUD data set.

The performance of FLAP (Fingerprints for Ligands and Proteins) in virtual screening is assessed using a subset of the DUD (Directory of Useful Decoys) benchmarking data set containing 13 targets each with more than 15 different chemotype classes. A variety of ligand and receptor-based virtual screening approaches are examined, using combinations of individual templates 2D structures of known actives, a cocrystallized ligand, a receptor structure, or a cocrystallized ligand-biased receptor structure. We examine several data fusion approaches to combine the results of the individual virtual screens. In doing so, we show that excellent chemotype enrichment is achieved in both single target ligand-based and receptor-based approaches, of approximately 17-fold over random on average at a false positive rate of 1%. We also show that using as much starting knowledge as possible improves chemotype enrichment, and that data fusion using Pareto ranking is an effective method to do this giving up to 50% improvement in enrichment over the single methods. Finally we show that if inactivity or decoy data is incorporated, automatically training the scoring function in FLAP improves recovery still further, with almost 2-fold improvement over the enrichments shown by the single methods. The results clearly demonstrate the utility of FLAP for virtual screening when either a limited or wide range of prior knowledge is available.