A submerged Stone Age hunting architecture from the Western Baltic Sea.

The Baltic Sea basins, some of which only submerged in the mid-Holocene, preserve Stone Age structures that did not survive on land. Yet, the discovery of these features is challenging and requires cross-disciplinary approaches between archeology and marine geosciences. Here, we combine shipborne and autonomousunderwater vehicle hydroacoustic data with up to a centimeter range resolution, sedimentological samples, and optical images to explore a Stone Age megastructure located in 21 m water depth in the Bay of Mecklenburg, Germany.

Molecular similarity concepts and search calculations.

The introduction of molecular similarity analysis in the early 1990s has catalyzed the development of many small-molecule-based similarity methods to mine large compound databases for novel active molecules. These efforts have profoundly influenced the field of computer-aided drug discovery and substantially widened the spectrum of available ligand-based virtual screening approaches. However, the principles underlying the computational assessment of molecular similarity are much more multifaceted and complex than it might appear at first glance.

Distinguishing between bioactive and modeled compound conformations through mining of emerging chemical patterns.

To systematically compare bioactive and theoretically derived compound conformations, we have analyzed 18 different sets of active small molecules with experimentally determined binding conformations and modeled conformers using a pattern recognition approach. Compound class-specific descriptor value range patterns that accurately distinguish bioactive conformations from other low-energy conformers were identified for all 18 compound classes. Discriminatory patterns were often chemically intuitive and could be well rationalized on the basis of X-ray structures of the protein-ligand complexes.

Formal concept analysis for the identification of molecular fragment combinations specific for active and highly potent compounds.

We introduce fragment formal concept analysis (FragFCA) to study complex relationships between fragments in active compounds taking potency information into account. Fragment combinations that are unique to active or highly potent compounds or that are shared by molecules having different or overlapping activity profiles are systematically identified using chemically intuitive queries of varying complexity. The methodology is applied to analyze fragment distributions in antagonists of seven G protein coupled receptor targets and identify signature fragments.

Exploring structure-selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping.

We design and analyze compound selectivity sets of antagonists with differential selectivity against seven biogenic amine G-protein coupled receptors. The selectivity sets consist of a total of 267 antagonists and contain a spectrum of in part closely related molecular scaffolds. Each set represents a different selectivity profile.

Simulation of sequential screening experiments using emerging chemical patterns.

A method called "Emerging Chemical Patterns" (ECP) has recently been introduced as a novel approach to binary molecular classification (for example, "active" versus "inactive"). The underlying pattern recognition algorithm was first introduced in computer science and then adopted for applications in medicinal chemistry and compound screening. A special feature is its ability to accurately classify molecules on the basis of very small training sets containing only a few compounds.

Emerging chemical patterns: a new methodology for molecular classification and compound selection.

A concept termed Emerging Chemical Patterns (ECPs) is introduced as a novel approach to molecular classification. The methodology makes it possible to extract key molecular features from very few known active compounds and classify molecules according to different potency levels. The approach was developed in light of the situation often faced during the early stages of lead optimization efforts: too few active reference molecules are available to build computational models for the prediction of potent compounds.